[{"id": "skill:nexu-io/open-design/design-templates/clinical-case-report", "name": "clinical-case-report", "kind": "skill", "official": false, "desc": "Structured medical case presentation for clinical rounds, conferences, and documentation. Generates SOAP-format or narrative case reports with physiologically accurate vitals, labs, and evidence-based plans. Use when the brief mentions \"case report\", \"case presentation\", \"SOAP note\", \"clinical case\", \"ward rounds\", \"case summary\", or \"patient presentation\".", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/clinical-case-report", "install": ["npx skills add nexu-io/open-design --skill clinical-case-report"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "clinical case report", "live": "Not checked (runs locally)"}, {"id": "skill:nexu-io/open-design/design-templates/html-ppt-zhangzara-monochrome", "name": "html-ppt-zhangzara-monochrome", "kind": "skill", "official": false, "desc": "A grant proposal on CRISPR base-editing for sickle-cell disease — the hypothesis, the approach, the milestones, and the risk. Built as a decision-grade academic research deck for grant review committee.", "repo": "https://github.com/nexu-io/open-design/tree/HEAD/design-templates/html-ppt-zhangzara-monochrome", "install": ["npx skills add nexu-io/open-design --skill html-ppt-zhangzara-monochrome"], "stars": 107550, "pushed": "2026-11-14T01:09:21Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/13c-metabolic-flux", "name": "13c-metabolic-flux", "kind": "skill", "official": false, "desc": "Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments ", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/13c-metabolic-flux", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/adaptyv", "name": "adaptyv", "kind": "skill", "official": false, "desc": "Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/adaptyv", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill adaptyv"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/alphagenome", "name": "alphagenome", "kind": "skill", "official": false, "desc": "Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when t", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/alphagenome", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill alphagenome"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/analytical-method-validation", "name": "analytical-method-validation", "kind": "skill", "official": false, "desc": "Plans, executes, and documents validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include \"method validation\", \"analytical method validation\", \"AMV\", \"validation ", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/analytical-method-validation", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill analytical-method-validation"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/clinical-decision-support", "name": "clinical-decision-support", "kind": "skill", "official": false, "desc": "Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Supports aggregate or synthetic research documentation and traceability, excluding patient care and live clinical operation.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/clinical-decision-support", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill clinical-decision-support"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/clinical-reports", "name": "clinical-reports", "kind": "skill", "official": false, "desc": "Creates safety-bounded draft structures and runs local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/clinical-reports", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill clinical-reports"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/mageck", "name": "mageck", "kind": "skill", "official": false, "desc": "Analyzes pooled CRISPR screen FASTQ reads and guide-count matrices with MAGeCK, validates guide libraries and contrasts, measures replicate and library QC, and produces gene hit rankings with effect sizes and FDR. Use for new knockout, CRISPRi, or CRISPRa screen analysis, enrichment or depletion contrasts, and MAGeCK count/test workflows; existing public dependency-score lookup belongs to DepMap.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/mageck", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill mageck"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "crispr screen fastq reads", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/marine-carbonate-chemistry", "name": "marine-carbonate-chemistry", "kind": "skill", "official": false, "desc": "Solves seawater carbonate chemistry with PyCO2SYS for chemical oceanography, ocean acidification, and marine carbon-cycle research. Use for paired total alkalinity, dissolved inorganic carbon, pH, or seawater pCO2/fCO2 measurements; carbonate speciation; aragonite and calcite saturation; Revelle factors; lab-to-in-situ temperature and pressure corrections; and measurement uncertainty propagation. Applies to carbonate-system calculations, not general aqueous speciation or air-sea gas-flux estimat", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/marine-carbonate-chemistry", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill marine-carbonate-chemistry"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "seawater carbonate chemistry", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/matchms", "name": "matchms", "kind": "skill", "official": false, "desc": "Processes, cleans, compares, and searches tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/matchms", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill matchms"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "tandem mass spectra", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/medchem", "name": "medchem", "kind": "skill", "official": false, "desc": "Applies medicinal chemistry filters for compound triage, using drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/medchem", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill medchem"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "compound triage", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/molecular-dynamics", "name": "molecular-dynamics", "kind": "skill", "official": false, "desc": "Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis. Sets up protein/small molecule systems, defines force fields, runs energy minimization and production MD, and analyzes trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/molecular-dynamics", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill molecular-dynamics"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "molecular dynamics simulations", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/molfeat", "name": "molfeat", "kind": "skill", "official": false, "desc": "Featurizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML. Covers ECFP/MACCS fingerprints, RDKit descriptors, pharmacophores, pretrained embeddings, configuration persistence, and molecule-to-label alignment.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/molfeat", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill molfeat"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "small molecules", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/neurokit2", "name": "neurokit2", "kind": "skill", "official": false, "desc": "Builds and audits reproducible NeuroKit2 research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Use when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/neurokit2", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill neurokit2"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "neurokit2", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/neuropixels-analysis", "name": "neuropixels-analysis", "kind": "skill", "official": false, "desc": "Analyzes Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/neuropixels-analysis", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill neuropixels-analysis"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "neuropixels extracellular recordings", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/nmrglue", "name": "nmrglue", "kind": "skill", "official": false, "desc": "Processes calibrated one-dimensional complex NMR free-induction decays with nmrglue into phased spectra, peak candidates, and signed integration regions. Use for raw 1D NMR processing, ppm-axis verification, apodization, Fourier transformation, manual phasing, baseline correction, or reproducible spectral integration.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/nmrglue", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill nmrglue"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "nmr free-induction decays", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/nwb-conversion", "name": "nwb-conversion", "kind": "skill", "official": false, "desc": "Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/nwb-conversion", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill nwb-conversion"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "neurodata without borders", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/onekgpd", "name": "onekgpd", "kind": "skill", "official": false, "desc": "Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individu", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/onekgpd", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill onekgpd"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "1000 genomes project", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/alphafold2", "name": "alphafold2", "kind": "skill", "official": false, "desc": "Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/alphafold2", "install": ["npx skills add aipoch/open-science --skill alphafold2"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/boltz", "name": "boltz", "kind": "skill", "official": false, "desc": "Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/boltz", "install": ["npx skills add aipoch/open-science --skill boltz"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/borzoi", "name": "borzoi", "kind": "skill", "official": false, "desc": "Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/borzoi", "install": ["npx skills add aipoch/open-science --skill borzoi"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/chai1", "name": "chai1", "kind": "skill", "official": false, "desc": "Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single FASTA, to re-fold designed binders as an AlphaFold-multimer alternative, or to drive co-folding from Python for batched campaigns on a GPU.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/chai1", "install": ["npx skills add aipoch/open-science --skill chai1"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/diffdock", "name": "diffdock", "kind": "skill", "official": false, "desc": "Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence model. Reach for this skill to dock a SMILES or SDF against a PDB, to generate ranked 3D poses for a small fragment library, or to get a starting pose for downstream rescoring. DiffDock predicts geometry, not affinity.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/diffdock", "install": ["npx skills add aipoch/open-science --skill diffdock"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/esmfold2", "name": "esmfold2", "kind": "skill", "official": false, "desc": "Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) P", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/esmfold2", "install": ["npx skills add aipoch/open-science --skill esmfold2"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/evo2", "name": "evo2", "kind": "skill", "official": false, "desc": "Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring regulatory or coding regions across species.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/evo2", "install": ["npx skills add aipoch/open-science --skill evo2"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/fair-esm2", "name": "fair-esm2", "kind": "skill", "official": false, "desc": "Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/fair-esm2", "install": ["npx skills add aipoch/open-science --skill fair-esm2"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/indication-dossier", "name": "indication-dossier", "kind": "skill", "official": false, "desc": "Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/indication-dossier", "install": ["npx skills add aipoch/open-science --skill indication-dossier"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/ligandmpnn", "name": "ligandmpnn", "kind": "skill", "official": false, "desc": "Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be respected, or to get threaded designed-sequence PDBs out of any MPNN run.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/ligandmpnn", "install": ["npx skills add aipoch/open-science --skill ligandmpnn"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/openfold3", "name": "openfold3", "kind": "skill", "official": false, "desc": "Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/openfold3", "install": ["npx skills add aipoch/open-science --skill openfold3"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/proteinmpnn", "name": "proteinmpnn", "kind": "skill", "official": false, "desc": "Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/proteinmpnn", "install": ["npx skills add aipoch/open-science --skill proteinmpnn"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/open-science/resources/skills/solublempnn", "name": "solublempnn", "kind": "skill", "official": false, "desc": "Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.", "repo": "https://github.com/aipoch/open-science/tree/HEAD/resources/skills/solublempnn", "install": ["npx skills add aipoch/open-science --skill solublempnn"], "stars": 5526, "pushed": "2026-11-14T01:41:01Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/etetoolkit", "name": "etetoolkit", "kind": "skill", "official": false, "desc": "Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/etetoolkit", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill etetoolkit"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/geo-database", "name": "geo-database", "kind": "skill", "official": false, "desc": "Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/geo-database", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill geo-database"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "ncbi geo", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/gtars", "name": "gtars", "kind": "skill", "official": false, "desc": "High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/gtars", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill gtars"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/histolab", "name": "histolab", "kind": "skill", "official": false, "desc": "Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/histolab", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill histolab"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/neuropixels-analysis", "name": "neuropixels-analysis", "kind": "skill", "official": false, "desc": "Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/neuropixels-analysis", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill neuropixels-analysis"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "neuropixels", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/pyhealth", "name": "pyhealth", "kind": "skill", "official": false, "desc": "Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/pyhealth", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill pyhealth"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "ehr", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/pyopenms", "name": "pyopenms", "kind": "skill", "official": false, "desc": "Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/pyopenms", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill pyopenms"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "openms", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/rdkit", "name": "rdkit", "kind": "skill", "official": false, "desc": "Core cheminformatics toolkit for SMILES/SDF/InChI parsing, descriptors (MW, LogP, TPSA), fingerprints, ECFP/Morgan fingerprints, substructure search, 2D/3D generation, similarity, reactions, and datamol-style molecule standardization when no separate wrapper skill is routed.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/rdkit", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill rdkit"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "rdkit", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/rowan", "name": "rowan", "kind": "skill", "official": false, "desc": "Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated compu", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/rowan", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill rowan"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "rowan", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/scanpy", "name": "scanpy", "kind": "skill", "official": false, "desc": "Single-cell RNA-seq and retained scverse workflow owner. Load .h5ad/10X data, manage AnnData metadata, plan scVI/scANVI batch-correction workflows, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/scanpy", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill scanpy"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "scanpy", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/ai-analyzer", "name": "ai-analyzer", "kind": "skill", "official": false, "desc": "AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/ai-analyzer", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill ai-analyzer"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-atac-seq-motif-deviation", "name": "bio-atac-seq-motif-deviation", "kind": "skill", "official": false, "desc": "Analyze transcription factor motif accessibility variability using chromVAR. Use when identifying which TF motifs show variable accessibility across samples or conditions in ATAC-seq data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-atac-seq-motif-deviation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-motif-deviation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-atac-seq-nucleosome-positioning", "name": "bio-atac-seq-nucleosome-positioning", "kind": "skill", "official": false, "desc": "Extract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis. Use when analyzing chromatin organization, identifying nucleosome-free regions at promoters, or characterizing nucleosome occupancy patterns from ATAC-seq fragment size distributions.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-atac-seq-nucleosome-positioning", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-nucleosome-positioning"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-basecalling", "name": "bio-basecalling", "kind": "skill", "official": false, "desc": "Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-basecalling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-basecalling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-batch-processing", "name": "bio-batch-processing", "kind": "skill", "official": false, "desc": "Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-batch-processing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-batch-processing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-causal-genomics-colocalization-analysis", "name": "bio-causal-genomics-colocalization-analysis", "kind": "skill", "official": false, "desc": "Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-causal-genomics-colocalization-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-colocalization-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-causal-genomics-fine-mapping", "name": "bio-causal-genomics-fine-mapping", "kind": "skill", "official": false, "desc": "Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-causal-genomics-fine-mapping", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-fine-mapping"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-causal-genomics-mediation-analysis", "name": "bio-causal-genomics-mediation-analysis", "kind": "skill", "official": false, "desc": "Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-causal-genomics-mediation-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mediation-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-causal-genomics-mendelian-randomization", "name": "bio-causal-genomics-mendelian-randomization", "kind": "skill", "official": false, "desc": "Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-causal-genomics-mendelian-randomization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-mendelian-randomization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-causal-genomics-pleiotropy-detection", "name": "bio-causal-genomics-pleiotropy-detection", "kind": "skill", "official": false, "desc": "Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instruments, or running sensitivity analyses for causal inference.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-causal-genomics-pleiotropy-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-causal-genomics-pleiotropy-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-cfdna-preprocessing", "name": "bio-cfdna-preprocessing", "kind": "skill", "official": false, "desc": "Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-cfdna-preprocessing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-cfdna-preprocessing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-differential-binding", "name": "bio-chipseq-differential-binding", "kind": "skill", "official": false, "desc": "Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-differential-binding", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-differential-binding"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-motif-analysis", "name": "bio-chipseq-motif-analysis", "kind": "skill", "official": false, "desc": "De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-motif-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-motif-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-peak-annotation", "name": "bio-chipseq-peak-annotation", "kind": "skill", "official": false, "desc": "Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-peak-annotation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-peak-annotation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-peak-calling", "name": "bio-chipseq-peak-calling", "kind": "skill", "official": false, "desc": "ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq alignments.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-peak-calling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-peak-calling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-qc", "name": "bio-chipseq-qc", "kind": "skill", "official": false, "desc": "ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-qc", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-qc"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-super-enhancers", "name": "bio-chipseq-super-enhancers", "kind": "skill", "official": false, "desc": "Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-super-enhancers", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-super-enhancers"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-chipseq-visualization", "name": "bio-chipseq-visualization", "kind": "skill", "official": false, "desc": "Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-chipseq-visualization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-visualization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-clinvar-lookup", "name": "bio-clinical-databases-clinvar-lookup", "kind": "skill", "official": false, "desc": "Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-clinvar-lookup", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-clinvar-lookup"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-dbsnp-queries", "name": "bio-clinical-databases-dbsnp-queries", "kind": "skill", "official": false, "desc": "Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-dbsnp-queries", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-dbsnp-queries"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-gnomad-frequencies", "name": "bio-clinical-databases-gnomad-frequencies", "kind": "skill", "official": false, "desc": "Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-gnomad-frequencies", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-gnomad-frequencies"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-hla-typing", "name": "bio-clinical-databases-hla-typing", "kind": "skill", "official": false, "desc": "Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-hla-typing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-hla-typing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-myvariant-queries", "name": "bio-clinical-databases-myvariant-queries", "kind": "skill", "official": false, "desc": "Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-myvariant-queries", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-myvariant-queries"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-pharmacogenomics", "name": "bio-clinical-databases-pharmacogenomics", "kind": "skill", "official": false, "desc": "Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-pharmacogenomics", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-pharmacogenomics"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-polygenic-risk", "name": "bio-clinical-databases-polygenic-risk", "kind": "skill", "official": false, "desc": "Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-polygenic-risk", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-polygenic-risk"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-somatic-signatures", "name": "bio-clinical-databases-somatic-signatures", "kind": "skill", "official": false, "desc": "Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-somatic-signatures", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-somatic-signatures"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-tumor-mutational-burden", "name": "bio-clinical-databases-tumor-mutational-burden", "kind": "skill", "official": false, "desc": "Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-tumor-mutational-burden", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-tumor-mutational-burden"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-clinical-databases-variant-prioritization", "name": "bio-clinical-databases-variant-prioritization", "kind": "skill", "official": false, "desc": "Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-clinical-databases-variant-prioritization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-clinical-databases-variant-prioritization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-copy-number-cnv-annotation", "name": "bio-copy-number-cnv-annotation", "kind": "skill", "official": false, "desc": "Annotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-copy-number-cnv-annotation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-copy-number-cnv-annotation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-copy-number-cnvkit-analysis", "name": "bio-copy-number-cnvkit-analysis", "kind": "skill", "official": false, "desc": "Detect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-copy-number-cnvkit-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-copy-number-cnvkit-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-copy-number-gatk-cnv", "name": "bio-copy-number-gatk-cnv", "kind": "skill", "official": false, "desc": "Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-copy-number-gatk-cnv", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-copy-number-gatk-cnv"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-base-editing-analysis", "name": "bio-crispr-screens-base-editing-analysis", "kind": "skill", "official": false, "desc": "Analyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-base-editing-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-base-editing-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-batch-correction", "name": "bio-crispr-screens-batch-correction", "kind": "skill", "official": false, "desc": "Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-batch-correction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-batch-correction"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-crispresso-editing", "name": "bio-crispr-screens-crispresso-editing", "kind": "skill", "official": false, "desc": "CRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-crispresso-editing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-crispresso-editing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-hit-calling", "name": "bio-crispr-screens-hit-calling", "kind": "skill", "official": false, "desc": "Statistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-hit-calling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-hit-calling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-jacks-analysis", "name": "bio-crispr-screens-jacks-analysis", "kind": "skill", "official": false, "desc": "JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-jacks-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-jacks-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-library-design", "name": "bio-crispr-screens-library-design", "kind": "skill", "official": false, "desc": "CRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-library-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-library-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-mageck-analysis", "name": "bio-crispr-screens-mageck-analysis", "kind": "skill", "official": false, "desc": "MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-mageck-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-mageck-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-crispr-screens-screen-qc", "name": "bio-crispr-screens-screen-qc", "kind": "skill", "official": false, "desc": "Quality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-crispr-screens-screen-qc", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-crispr-screens-screen-qc"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-ctdna-mutation-detection", "name": "bio-ctdna-mutation-detection", "kind": "skill", "official": false, "desc": "Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-ctdna-mutation-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-ctdna-mutation-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-epidemiological-genomics-amr-surveillance", "name": "bio-epidemiological-genomics-amr-surveillance", "kind": "skill", "official": false, "desc": "Detect and track antimicrobial resistance genes using AMRFinderPlus and ResFinder with epidemiological context. Monitor resistance trends and identify emerging resistance patterns. Use when screening genomes for AMR genes or tracking resistance in surveillance programs.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-epidemiological-genomics-amr-surveillance", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epidemiological-genomics-amr-surveillance"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-epidemiological-genomics-pathogen-typing", "name": "bio-epidemiological-genomics-pathogen-typing", "kind": "skill", "official": false, "desc": "Perform multi-locus sequence typing (MLST), core genome MLST, and SNP-based strain typing for bacterial isolate characterization using mlst and chewBBACA. Use when identifying strain types, tracking outbreak clones, or characterizing bacterial isolates.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-epidemiological-genomics-pathogen-typing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epidemiological-genomics-pathogen-typing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-epidemiological-genomics-phylodynamics", "name": "bio-epidemiological-genomics-phylodynamics", "kind": "skill", "official": false, "desc": "Construct time-scaled phylogenies and infer evolutionary dynamics using TreeTime and BEAST2 for outbreak analysis. Estimate divergence times, molecular clock rates, and ancestral states. Use when dating outbreak origins, estimating transmission rates, or building time-calibrated trees.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-epidemiological-genomics-phylodynamics", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epidemiological-genomics-phylodynamics"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-epidemiological-genomics-transmission-inference", "name": "bio-epidemiological-genomics-transmission-inference", "kind": "skill", "official": false, "desc": "Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or identifying superspreaders.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-epidemiological-genomics-transmission-inference", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epidemiological-genomics-transmission-inference"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-epidemiological-genomics-variant-surveillance", "name": "bio-epidemiological-genomics-variant-surveillance", "kind": "skill", "official": false, "desc": "Assign pathogen lineages and track variants using Nextclade and pangolin for viral surveillance. Monitor variant prevalence and identify emerging variants of concern. Use when classifying viral sequences, tracking lineage dynamics, or monitoring for variants of concern.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-epidemiological-genomics-variant-surveillance", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-epidemiological-genomics-variant-surveillance"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-fastq-quality", "name": "bio-fastq-quality", "kind": "skill", "official": false, "desc": "Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-fastq-quality", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-fastq-quality"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-filter-sequences", "name": "bio-filter-sequences", "kind": "skill", "official": false, "desc": "Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-filter-sequences", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-filter-sequences"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-bead-normalization", "name": "bio-flow-cytometry-bead-normalization", "kind": "skill", "official": false, "desc": "Bead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-bead-normalization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-bead-normalization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-clustering-phenotyping", "name": "bio-flow-cytometry-clustering-phenotyping", "kind": "skill", "official": false, "desc": "Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-clustering-phenotyping", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-clustering-phenotyping"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-compensation-transformation", "name": "bio-flow-cytometry-compensation-transformation", "kind": "skill", "official": false, "desc": "Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-compensation-transformation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-compensation-transformation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-cytometry-qc", "name": "bio-flow-cytometry-cytometry-qc", "kind": "skill", "official": false, "desc": "Comprehensive quality control for flow cytometry and CyTOF data. Covers flow rate stability, signal drift, margin events, dead cell exclusion, and batch QC. Use when assessing acquisition quality or identifying problematic samples before analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-cytometry-qc", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-cytometry-qc"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-differential-analysis", "name": "bio-flow-cytometry-differential-analysis", "kind": "skill", "official": false, "desc": "Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-differential-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-differential-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-doublet-detection", "name": "bio-flow-cytometry-doublet-detection", "kind": "skill", "official": false, "desc": "Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-doublet-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-doublet-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-fcs-handling", "name": "bio-flow-cytometry-fcs-handling", "kind": "skill", "official": false, "desc": "Read and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-fcs-handling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-fcs-handling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-flow-cytometry-gating-analysis", "name": "bio-flow-cytometry-gating-analysis", "kind": "skill", "official": false, "desc": "Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-flow-cytometry-gating-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-gating-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-format-conversion", "name": "bio-format-conversion", "kind": "skill", "official": false, "desc": "Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-format-conversion", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-format-conversion"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-fragment-analysis", "name": "bio-fragment-analysis", "kind": "skill", "official": false, "desc": "Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-fragment-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-fragment-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-gatk-variant-calling", "name": "bio-gatk-variant-calling", "kind": "skill", "official": false, "desc": "Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-gatk-variant-calling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-gatk-variant-calling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-genome-engineering-base-editing-design", "name": "bio-genome-engineering-base-editing-design", "kind": "skill", "official": false, "desc": "Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-genome-engineering-base-editing-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-genome-engineering-base-editing-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-genome-engineering-grna-design", "name": "bio-genome-engineering-grna-design", "kind": "skill", "official": false, "desc": "Design guide RNAs for CRISPR-Cas9/Cas12a experiments using CRISPRscan and local scoring algorithms. Score guides for on-target activity using Rule Set 2 and Azimuth models. Use when designing sgRNAs for gene knockout, activation, or repression experiments.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-genome-engineering-grna-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-genome-engineering-grna-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-genome-engineering-hdr-template-design", "name": "bio-genome-engineering-hdr-template-design", "kind": "skill", "official": false, "desc": "Design homology-directed repair donor templates for CRISPR knock-ins using primer3-py. Create ssODN, dsDNA, or plasmid templates with optimized homology arms. Use when designing donor templates for precise insertions, tagging, or allele replacement.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-genome-engineering-hdr-template-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-genome-engineering-hdr-template-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-genome-engineering-off-target-prediction", "name": "bio-genome-engineering-off-target-prediction", "kind": "skill", "official": false, "desc": "Predict CRISPR off-target sites using Cas-OFFinder and CFD scoring algorithms. Identify potential unintended cleavage sites genome-wide and assess guide specificity. Use when evaluating guide RNA specificity or selecting guides with minimal off-target risk.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-genome-engineering-off-target-prediction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-genome-engineering-off-target-prediction"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-genome-engineering-prime-editing-design", "name": "bio-genome-engineering-prime-editing-design", "kind": "skill", "official": false, "desc": "Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-genome-engineering-prime-editing-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-genome-engineering-prime-editing-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-hi-c-analysis-compartment-analysis", "name": "bio-hi-c-analysis-compartment-analysis", "kind": "skill", "official": false, "desc": "Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-compartment-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-compartment-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-hi-c-analysis-contact-pairs", "name": "bio-hi-c-analysis-contact-pairs", "kind": "skill", "official": false, "desc": "Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-contact-pairs", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-contact-pairs"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-hi-c-analysis-hic-data-io", "name": "bio-hi-c-analysis-hic-data-io", "kind": "skill", "official": false, "desc": "Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-hic-data-io", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-hic-data-io"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/claude-scientific-writer/.claude/skills/clinical-decision-support", "name": "clinical-decision-support", "kind": "skill", "official": false, "desc": "Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.", "repo": "https://github.com/K-Dense-AI/claude-scientific-writer/tree/HEAD/.claude/skills/clinical-decision-support", "install": ["npx skills add K-Dense-AI/claude-scientific-writer --skill clinical-decision-support"], "stars": 2576, "pushed": "2026-11-11T09:24:08Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/claude-scientific-writer/.claude/skills/clinical-reports", "name": "clinical-reports", "kind": "skill", "official": false, "desc": "Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.", "repo": "https://github.com/K-Dense-AI/claude-scientific-writer/tree/HEAD/.claude/skills/clinical-reports", "install": ["npx skills add K-Dense-AI/claude-scientific-writer --skill clinical-reports"], "stars": 2576, "pushed": "2026-11-11T09:24:08Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/claude-scientific-writer/.claude/skills/scientific-schematics", "name": "scientific-schematics", "kind": "skill", "official": false, "desc": "Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.", "repo": "https://github.com/K-Dense-AI/claude-scientific-writer/tree/HEAD/.claude/skills/scientific-schematics", "install": ["npx skills add K-Dense-AI/claude-scientific-writer --skill scientific-schematics"], "stars": 2576, "pushed": "2026-11-11T09:24:08Z", "license": "MIT", "plat": "nano banana 2", "live": "Not checked (runs locally)"}, {"id": "skill:JuneYaooo/nihaisha-nishi-tcm", "name": "nihaisha", "kind": "skill", "official": false, "desc": "Use this skill when the user asks about Ni Haisha / 倪海厦 TCM course material, especially Shang Han Lun / 伤寒论, Jingui / 金匮要略, Zhongjing Xinfa / 仲景心法, clinical cases / 临床案例 / 倪师医案, Bagang Bianzheng / 八纲辨证, Fuyang Forum / 扶阳论坛, Yijinjing / 易筋经, Liang Dong dialogue / 梁冬对话倪师, Stanford lecture / 斯坦福大学演讲, Tianji / 天纪 / 易经 / 阳宅 / 紫微斗数, Huangdi Neijing / 黄帝内经, Shennong Bencao / 神农本草, acupuncture / 针灸, meridians, acupoints, acupuncture depth safety / 危险进针深度 / 气胸风险, six-channel pattern identification, sympt", "repo": "https://github.com/JuneYaooo/nihaisha-nishi-tcm", "install": ["npx skills add JuneYaooo/nihaisha-nishi-tcm --skill nihaisha"], "stars": 2250, "pushed": "2026-11-07T14:53:06Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/addiction-self-assessment", "name": "addiction-self-assessment", "kind": "skill", "official": false, "desc": "Substance use and behavioral addiction self-assessment and harm reduction guidance. Use when someone suspects their drinking or substance use has become a problem, wants an honest self-assessment, or needs help finding affordable treatment.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/addiction-self-assessment", "install": ["npx skills add LeoYeAI/agent-master-skills --skill addiction-self-assessment"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/adme-property-predictor", "name": "adme-property-predictor", "kind": "skill", "official": false, "desc": "Predict ADME (Absorption, Distribution, Metabolism, Excretion) properties for drug candidates using cheminformatics models and molecular descriptors. Evaluates drug-likeness, bioavailability, and pharmacokinetic profile to guide lead optimization and candidate selection in drug discovery.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/adme-property-predictor", "install": ["npx skills add LeoYeAI/agent-master-skills --skill adme-property-predictor"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:LeoYeAI/agent-master-skills/skills/afrexai-personal-finance", "name": "afrexai-personal-finance", "kind": "skill", "official": false, "desc": "Complete personal finance system — budgeting, debt payoff, investing, tax optimization, net worth tracking, and financial independence planning. Use when managing money, building wealth, paying off debt, planning retirement, or optimizing taxes. Zero dependencies.", "repo": "https://github.com/LeoYeAI/agent-master-skills/tree/HEAD/skills/afrexai-personal-finance", "install": ["npx skills add LeoYeAI/agent-master-skills --skill afrexai-personal-finance"], "stars": 2192, "pushed": "2026-11-13T02:01:09Z", "license": "MIT", "plat": "afrexai-personal-finance", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/active-comparator-single-soc-faers-safety-comparison", "name": "active-comparator-single-soc-faers-safety-comparison", "kind": "skill", "official": false, "desc": "Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/active-comparator-single-soc-faers-safety-comparison", "install": ["npx skills add aipoch/medical-research-skills --skill active-comparator-single-soc-faers-safety-comparison"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/aim-and-hypothesis-designer", "name": "aim-and-hypothesis-designer", "kind": "skill", "official": false, "desc": "Designs primary aims, secondary aims, and testable hypotheses from broad biomedical research ideas. Use this skill when a user needs to convert a loose study idea into a tighter protocol-framing structure with clear aim hierarchy, hypothesis discipline, and separation between hypothesis-driven and exploratory components. Always keep aims answerable, non-overlapping, and aligned to the intended evidence type and study scope.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/aim-and-hypothesis-designer", "install": ["npx skills add aipoch/medical-research-skills --skill aim-and-hypothesis-designer"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/animal-and-cell-validation-planner", "name": "animal-and-cell-validation-planner", "kind": "skill", "official": false, "desc": "Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-s", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/animal-and-cell-validation-planner", "install": ["npx skills add aipoch/medical-research-skills --skill animal-and-cell-validation-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Other/bianque", "name": "bianque", "kind": "skill", "official": false, "desc": "Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching dise", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Other/bianque", "install": ["npx skills add aipoch/medical-research-skills --skill bianque"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/bidirectional-multi-phenotype-mr-research-planner", "name": "bidirectional-multi-phenotype-mr-research-planner", "kind": "skill", "official": false, "desc": "Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO s", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/bidirectional-multi-phenotype-mr-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill bidirectional-multi-phenotype-mr-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/bulk-omics-integrative-planner", "name": "bulk-omics-integrative-planner", "kind": "skill", "official": false, "desc": "Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example da", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/bulk-omics-integrative-planner", "install": ["npx skills add aipoch/medical-research-skills --skill bulk-omics-integrative-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/case-control-study-planner", "name": "case-control-study-planner", "kind": "skill", "official": false, "desc": "Design a structured case-control study framework with explicit source population logic, control selection rules, matching decisions, exposure measurement planning, and bias-control checkpoints.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/case-control-study-planner", "install": ["npx skills add aipoch/medical-research-skills --skill case-control-study-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/clinical-cohort-protocol-designer", "name": "clinical-cohort-protocol-designer", "kind": "skill", "official": false, "desc": "Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data avai", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/clinical-cohort-protocol-designer", "install": ["npx skills add aipoch/medical-research-skills --skill clinical-cohort-protocol-designer"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/comorbidity-common-immune-biomarker-research-planner", "name": "comorbidity-common-immune-biomarker-research-planner", "kind": "skill", "official": false, "desc": "Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarke", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/comorbidity-common-immune-biomarker-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill comorbidity-common-immune-biomarker-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/comparative-network-toxicology-shared-mechanism-reference-grounded", "name": "comparative-network-toxicology-shared-mechanism-reference-grounded", "kind": "skill", "official": false, "desc": "Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/comparative-network-toxicology-shared-mechanism-reference-grounded", "install": ["npx skills add aipoch/medical-research-skills --skill comparative-network-toxicology-shared-mechanism-reference-grounded"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/confounder-and-bias-control-planner", "name": "confounder-and-bias-control-planner", "kind": "skill", "official": false, "desc": "Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal stru", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/confounder-and-bias-control-planner", "install": ["npx skills add aipoch/medical-research-skills --skill confounder-and-bias-control-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/conventional-non-oncology-hub-gene-research-planner", "name": "conventional-non-oncology-hub-gene-research-planner", "kind": "skill", "official": false, "desc": "Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG dis", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/conventional-non-oncology-hub-gene-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill conventional-non-oncology-hub-gene-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/conventional-oncology-hub-gene-research-planner", "name": "conventional-oncology-hub-gene-research-planner", "kind": "skill", "official": false, "desc": "Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell va", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/conventional-oncology-hub-gene-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill conventional-oncology-hub-gene-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/cross-disease-shared-biomarker-network-research-planner", "name": "cross-disease-shared-biomarker-network-research-planner", "kind": "skill", "official": false, "desc": "Generates complete cross-disease shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Always use this skill whenever a user wants to design, plan, or build a multi-dataset study linking two related diseases through shared DEGs, enrichment, PPI hub genes, public validation, regulatory-network analysis, immune infiltration, drug-gene interaction screening, and optional qRT-PCR or cell-line validation. Covers five study patterns (shared-DEG di", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/cross-disease-shared-biomarker-network-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill cross-disease-shared-biomarker-network-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner", "name": "drug-repurposing-study-planner", "kind": "skill", "official": false, "desc": "Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner", "install": ["npx skills add aipoch/medical-research-skills --skill drug-repurposing-study-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/dual-disease-shared-transcriptome-biomarker-research-planner", "name": "dual-disease-shared-transcriptome-biomarker-research-planner", "kind": "skill", "official": false, "desc": "Generates complete dual-disease shared-transcriptome biomarker and hub-gene research designs from a user-provided disease pair and shared-biology direction. Always use this skill whenever a user wants to design, plan, or build a non-oncology two-disease transcriptome study centered on per-disease differential expression, shared-signal intersection or concordance, PPI-based hub-gene prioritization, diagnostic evaluation across both diseases, immune infiltration context, pathway interpretation, an", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/dual-disease-shared-transcriptome-biomarker-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill dual-disease-shared-transcriptome-biomarker-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/endpoint-definition-designer", "name": "endpoint-definition-designer", "kind": "skill", "official": false, "desc": "Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/endpoint-definition-designer", "install": ["npx skills add aipoch/medical-research-skills --skill endpoint-definition-designer"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/faers-pharmacovigilance-disproportionality-research-planner", "name": "faers-pharmacovigilance-disproportionality-research-planner", "kind": "skill", "official": false, "desc": "Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/faers-pharmacovigilance-disproportionality-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill faers-pharmacovigilance-disproportionality-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/feasibility-aware-study-planner", "name": "feasibility-aware-study-planner", "kind": "skill", "official": false, "desc": "Designs a realistic, execution-aware biomedical study version under explicit constraints of samples, time, budget, data access, lab capacity, team skill, and validation resources. Always use this skill when the user has a real study idea, a candidate route, or a partially framed project but cannot assume ideal conditions. If critical feasibility inputs are missing, first clarify what resources are currently available, what resources may be obtainable, and what resources are realistically unavail", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/feasibility-aware-study-planner", "install": ["npx skills add aipoch/medical-research-skills --skill feasibility-aware-study-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/generic-phenotype-scoring-research-planner", "name": "generic-phenotype-scoring-research-planner", "kind": "skill", "official": false, "desc": "Generates complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signatu", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/generic-phenotype-scoring-research-planner", "install": ["npx skills add aipoch/medical-research-skills --skill generic-phenotype-scoring-research-planner"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Protocol Design/inclusion-exclusion-criteria-builder", "name": "inclusion-exclusion-criteria-builder", "kind": "skill", "official": false, "desc": "Builds clear, executable, and auditable inclusion and exclusion criteria for biomedical and clinical research protocols. Always use this skill when a user needs to translate a target population into operational screening rules tied to chart fields, time windows, tests, procedures, prior therapies, exclusions, and reviewable edge cases. Focus on protocol-stage precision, ambiguity reduction, auditability, and screening reproducibility rather than generic study design advice.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Protocol Design/inclusion-exclusion-criteria-builder", "install": ["npx skills add aipoch/medical-research-skills --skill inclusion-exclusion-criteria-builder"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Other/mendel", "name": "mendel", "kind": "skill", "official": false, "desc": "Gregor Mendel — genetics mentor, patient experimenter, and gardener-monk. Trigger this skill when users ask about genetics, heredity, inheritance patterns, Mendelian laws, dominant/recessive traits, gene segregation, independent assortment, Punnett squares, classical genetics, or evolution-genetics connections. Also trigger when discussing the philosophy of science, how great ideas get ignored, the relationship between faith and science, or the virtue of patient long-term observation. Even if th", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Other/mendel", "install": ["npx skills add aipoch/medical-research-skills --skill mendel"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Evidence Insight/study-design-identifier", "name": "study-design-identifier", "kind": "skill", "official": false, "desc": "Identifies the real underlying study design used in a medical or biomedical paper, distinguishes primary and secondary design components when papers are hybrid, and converts the paper into an evidence-aware design label suitable for literature appraisal, evidence grading, and downstream review workflows. Always identify the actual design from what the study did, not from how the authors describe it. Never fabricate references, metadata, or study features.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Evidence Insight/study-design-identifier", "install": ["npx skills add aipoch/medical-research-skills --skill study-design-identifier"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Evidence Insight/topic-evidence-mapper", "name": "topic-evidence-mapper", "kind": "skill", "official": false, "desc": "Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Evidence Insight/topic-evidence-mapper", "install": ["npx skills add aipoch/medical-research-skills --skill topic-evidence-mapper"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:aipoch/medical-research-skills/awesome-med-research-skills/Evidence Insight/unmet-clinical-need-extractor", "name": "unmet-clinical-need-extractor", "kind": "skill", "official": false, "desc": "Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from gener", "repo": "https://github.com/aipoch/medical-research-skills/tree/HEAD/awesome-med-research-skills/Evidence Insight/unmet-clinical-need-extractor", "install": ["npx skills add aipoch/medical-research-skills --skill unmet-clinical-need-extractor"], "stars": 2054, "pushed": "2026-09-17T05:39:52Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FANzR-arch/Numerologist_skills/bazi", "name": "bazi", "kind": "skill", "official": false, "desc": "四柱八字专业排盘与解盘技能。当用户提到八字、四柱、天干地支、日主、十神、 大运流年、喜用神、格局、伤官配印、食神生财、五行旺衰、命局分析等相关内容时 触发此技能。也适用于用户想了解自己的命运、性格、事业、婚姻、财运等， 且明确要求或暗示使用八字体系的场景。包含完整的排盘、格局判断、十神分析、 大运流年推演等能力。", "repo": "https://github.com/FANzR-arch/Numerologist_skills/tree/HEAD/bazi", "install": ["npx skills add FANzR-arch/Numerologist_skills --skill bazi"], "stars": 1617, "pushed": "2026-10-16T12:01:29Z", "license": null, "plat": "bazi", "live": "Not checked (runs locally)"}, {"id": "skill:FANzR-arch/Numerologist_skills/qimen-dunjia", "name": "qimen-dunjia", "kind": "skill", "official": false, "desc": "奇门遁甲排盘、解盘、择时、方位判断与理论教学技能。用户明确提到奇门遁甲、时家奇门、 奇门排盘、奇门解盘、奇门择吉、方位选择、趋吉避凶，或要求“用奇门”分析一件具体事情时， 必须使用本技能。正式排盘前先做结构化访谈，再调用脚本完成固定计算，最后再做解读与建议。", "repo": "https://github.com/FANzR-arch/Numerologist_skills/tree/HEAD/qimen-dunjia", "install": ["npx skills add FANzR-arch/Numerologist_skills --skill qimen-dunjia"], "stars": 1617, "pushed": "2026-10-16T12:01:29Z", "license": null, "plat": "qimen dunjia", "live": "Not checked (runs locally)"}, {"id": "skill:FANzR-arch/Numerologist_skills/ziwei-doushu", "name": "ziwei-doushu", "kind": "skill", "official": false, "desc": "紫微斗数专业排盘与解盘技能。当用户提到紫微斗数、紫微排盘、紫微命盘、命宫、 十二宫、主星、四化、大限、流年、斗数格局、飞星、三方四正、紫微星、天机星、 太阳星、武曲星、天同星、廉贞星、天府星、太阴星、贪狼星、巨门星、天相星、 天梁星、七杀星、破军星等相关内容时触发此技能。也适用于用户想了解自己的命运、 性格、事业、婚姻、财运等，且明确要求或暗示使用紫微斗数体系的场景。包含完整的 排盘、宫位解读、四化分析、大限流年推演等能力。正式排盘调用脚本完成固定计算， 模型只负责访谈与解读。", "repo": "https://github.com/FANzR-arch/Numerologist_skills/tree/HEAD/ziwei-doushu", "install": ["npx skills add FANzR-arch/Numerologist_skills --skill ziwei-doushu"], "stars": 1617, "pushed": "2026-10-16T12:01:29Z", "license": null, "plat": "ziwei doushu", "live": "Not checked (runs locally)"}, {"id": "mcp:ai.thetahealth/mirobody", "name": "Mirobody", "kind": "mcp", "official": null, "desc": "Lab results to LOINC codes and UCUM units, symptoms and diagnoses to ICPC-3. Offline, no API key.", "repo": "https://github.com/thetahealth/mirobody", "install": ["pypi:mirobody"], "stars": 1492, "pushed": "2026-11-13T05:35:26Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:luwill/research-skills/medical-imaging-review", "name": "medical-imaging-review", "kind": "skill", "official": false, "desc": "Use when the user asks to plan, conduct, document, synthesize, or draft any part of a medical imaging AI literature review (narrative review or method survey, scoping review, or systematic review with qualitative synthesis), including a single section or introduction, protocol design, literature search, screening, extraction, risk-of-bias appraisal, evidence synthesis, or the methods and results write-up; Chinese requests such as 综述, 文献综述, 综述章节, 系统综述 or 范围综述 count. Do not use this skill to perfo", "repo": "https://github.com/luwill/research-skills/tree/HEAD/medical-imaging-review", "install": ["npx skills add luwill/research-skills --skill medical-imaging-review"], "stars": 887, "pushed": "2026-11-06T03:28:08Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:agentara/skills/skills/health/emergency-triage", "name": "emergency-triage", "kind": "skill", "official": false, "desc": "急诊分诊与症状排查助手。当用户描述身体不适、疼痛、突发症状，或者说\"我哪里痛\"、\"我不舒服\"、\"该挂什么科\"、\"需要去医院吗\"、\"帮我判断一下\"等与身体健康相关的紧急或半紧急问题时，触发此 skill。也适用于用户帮家人朋友做初步症状排查的场景。核心能力：通过假设-排除式交互问诊快速缩小可能病因范围，给出病因可能性排行、挂号科室建议和到院后应做的检查项目。帮用户在就医前做好信息准备，减少在医院的沟通成本。", "repo": "https://github.com/agentara/skills/tree/HEAD/skills/health/emergency-triage", "install": ["npx skills add agentara/skills --skill emergency-triage"], "stars": 629, "pushed": "2026-09-29T12:49:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:agentara/skills/skills/health/lab-interpreter", "name": "lab-interpreter", "kind": "skill", "official": false, "desc": "Interpret medical lab/test reports (blood panels, urine, liver/kidney function, thyroid, tumor markers, coagulation, cardiac enzymes, hormones, etc.) uploaded as images, PDFs, or text. Trigger whenever the user uploads a lab report, medical test result, or clinical diagnostic sheet — or says things like \"help me read this report\", \"what do these results mean\", \"化验单\", \"检验报告\", \"帮我看看这个报告\", \"blood test results\", \"lab results\", \"体检报告\", \"检查报告单\", \"血常规\", \"尿常规\", \"肝功能\", \"肾功能\", \"甲功\", \"凝血\", \"interpret my la", "repo": "https://github.com/agentara/skills/tree/HEAD/skills/health/lab-interpreter", "install": ["npx skills add agentara/skills --skill lab-interpreter"], "stars": 629, "pushed": "2026-09-29T12:49:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:agentara/skills/skills/health/super-daddy", "name": "super-daddy", "kind": "skill", "official": false, "desc": "Use for Chinese-language or China-family-context family-doctor-style parenting and child health management: 育儿, 宝宝护理, 科学养育, 生长发育, 喂养, 母乳, 配方奶, 辅食, 睡眠, 湿疹, 过敏观察, 疫苗沟通, 居家观察, 宝宝档案, parenting.md tracking, 看病问诊, 病例归档, 父母焦虑, 家庭协作, visual parenting plans, or dad-focused caregiving. Also use as a safety bridge when a caregiver raises acute child symptoms in a parenting context. Do not use for diagnosis, prescriptions, medication dosing, lab reports, or adult-only health questions.", "repo": "https://github.com/agentara/skills/tree/HEAD/skills/health/super-daddy", "install": ["npx skills add agentara/skills --skill super-daddy"], "stars": 629, "pushed": "2026-09-29T12:49:29Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.neiltron/apple-health-mcp", "name": "Apple Health", "kind": "mcp", "official": null, "desc": "Local-first Apple Health MCP server for questions about your sleep, workouts and heart data.", "repo": "https://github.com/neiltron/apple-health-mcp", "install": ["npm:@neiltron/apple-health-mcp"], "stars": 575, "pushed": "2026-10-02T03:55:25Z", "license": "MIT", "plat": "apple health", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.chrisdoc/hevy-mcp", "name": "Hevy MCP Server", "kind": "mcp", "official": null, "desc": "MCP server for managing workouts, routines, and exercise data through the Hevy API", "repo": "https://github.com/chrisdoc/hevy-mcp", "install": ["npm:hevy-mcp"], "stars": 539, "pushed": "2026-11-11T06:04:45Z", "license": "MIT", "plat": "hevy", "live": "Not checked (runs locally)"}, {"id": "skill:Google-Health-API/google-health-cli/skills/ghealth", "name": "ghealth", "kind": "skill", "official": false, "desc": "Query Google Health API v4 — steps, heart rate, exercise, sleep, weight, SpO2, HRV, ECG, blood glucose, nutrition, and 40 total data types", "repo": "https://github.com/Google-Health-API/google-health-cli/tree/HEAD/skills/ghealth", "install": ["npx skills add Google-Health-API/google-health-cli --skill ghealth"], "stars": 284, "pushed": "2026-07-10T09:03:30Z", "license": "Apache-2.0", "plat": "google health", "live": "Not checked (runs locally)"}, {"id": "skill:artwist-polyakov/polyakov-claude-skills/plugins/genome-analizer/skills/genome-analizer", "name": "genome-analyzer", "kind": "skill", "official": false, "desc": "Анализирует генетические данные пользователя из VCF файла. Используй когда пользователь спрашивает о своей генетике, наследственных признаках, предрасположенностях, метаболизме веществ (кофеин, алкоголь, лекарства), спортивных способностях, рисках заболеваний, питании на основе генов.", "repo": "https://github.com/artwist-polyakov/polyakov-claude-skills/tree/HEAD/plugins/genome-analizer/skills/genome-analizer", "install": ["npx skills add artwist-polyakov/polyakov-claude-skills --skill genome-analyzer"], "stars": 234, "pushed": "2026-11-05T17:29:50Z", "license": "MIT", "plat": "vcf", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/boltz", "name": "boltz", "kind": "skill", "official": false, "desc": "Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/boltz", "install": ["npx skills add adaptyvbio/protein-design-skills --skill boltz"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/boltzgen", "name": "boltzgen", "kind": "skill", "official": false, "desc": "All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/boltzgen", "install": ["npx skills add adaptyvbio/protein-design-skills --skill boltzgen"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/campaign-manager", "name": "campaign-manager", "kind": "skill", "official": false, "desc": "Goal-oriented binder design campaign planning and health assessment. Use this skill when: (1) Planning a complete binder design campaign, (2) Converting high-level goals into runnable pipelines, (3) Assessing campaign health and pass rates, (4) Diagnosing why designs are failing QC, (5) Estimating time, cost, and expected yields, (6) Selecting between design tools for a specific target. This skill orchestrates the other protein design tools. For individual tool parameters, use the specific tool ", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/campaign-manager", "install": ["npx skills add adaptyvbio/protein-design-skills --skill campaign-manager"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/cell-free-expression", "name": "cell-free-expression", "kind": "skill", "official": false, "desc": "Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/cell-free-expression", "install": ["npx skills add adaptyvbio/protein-design-skills --skill cell-free-expression"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/chai", "name": "chai", "kind": "skill", "official": false, "desc": "Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/chai", "install": ["npx skills add adaptyvbio/protein-design-skills --skill chai"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/esm", "name": "esm", "kind": "skill", "official": false, "desc": "ESM protein language models for embeddings, sequence scoring, structure prediction, and binder design. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) or mutation-effect scores, (2) Getting protein embeddings for clustering or filtering, (3) Predicting complex structures with ESMFold2, (4) Designing binders by inverting ESMFold2, (5) Filtering designs by sequence plausibility. For diffusion-based structure prediction, use boltz or chai. For QC thresholds, use protein-qc. For gradi", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/esm", "install": ["npx skills add adaptyvbio/protein-design-skills --skill esm"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/foldseek", "name": "foldseek", "kind": "skill", "official": false, "desc": "Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/foldseek", "install": ["npx skills add adaptyvbio/protein-design-skills --skill foldseek"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/germinal", "name": "germinal", "kind": "skill", "official": false, "desc": "De novo antibody and nanobody (VHH) design with Germinal. Use this skill when: (1) Designing epitope-targeted nanobodies or scFvs, (2) Needing CDR design on a fixed framework, (3) Working on antibody-format binders rather than miniproteins. For miniprotein binders, use binder-design (BoltzGen, BindCraft, RFdiffusion, Mosaic). For structure validation, use boltz or chai.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/germinal", "install": ["npx skills add adaptyvbio/protein-design-skills --skill germinal"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/ipsae", "name": "ipsae", "kind": "skill", "official": false, "desc": "Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/ipsae", "install": ["npx skills add adaptyvbio/protein-design-skills --skill ipsae"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/ligandmpnn", "name": "ligandmpnn", "kind": "skill", "official": false, "desc": "Ligand-aware protein sequence design using LigandMPNN. Use this skill when: (1) Designing sequences around small molecules, (2) Enzyme active site design, (3) Ligand binding pocket optimization, (4) Metal coordination site design, (5) Cofactor binding proteins. For standard protein design, use proteinmpnn. For solubility optimization, use solublempnn.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/ligandmpnn", "install": ["npx skills add adaptyvbio/protein-design-skills --skill ligandmpnn"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/mosaic", "name": "mosaic", "kind": "skill", "official": false, "desc": "Multi-objective, gradient-based protein binder design with Mosaic. Use this skill when: (1) Composing several structure or sequence models into one design objective, (2) Optimizing binders against a custom loss rather than a fixed pipeline, (3) Wanting gradient descent over sequence space in the style of ColabDesign, RSO, or BindCraft but with interchangeable predictors, (4) Letting the optimizer choose the epitope instead of fixing hotspots. For an end-to-end binder pipeline with default filter", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/mosaic", "install": ["npx skills add adaptyvbio/protein-design-skills --skill mosaic"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/pdb", "name": "pdb", "kind": "skill", "official": false, "desc": "Fetch and analyze protein structures from RCSB PDB. Use this skill when: (1) Need to download a structure by PDB ID, (2) Search for similar structures, (3) Prepare target for binder design, (4) Extract specific chains or domains, (5) Get structure metadata. For sequence lookup, use uniprot. For binder design workflow, use binder-design.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/pdb", "install": ["npx skills add adaptyvbio/protein-design-skills --skill pdb"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/protein-design-workflow", "name": "protein-design-workflow", "kind": "skill", "official": false, "desc": "End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/protein-design-workflow", "install": ["npx skills add adaptyvbio/protein-design-skills --skill protein-design-workflow"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/protein-qc", "name": "protein-qc", "kind": "skill", "official": false, "desc": "Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scor", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/protein-qc", "install": ["npx skills add adaptyvbio/protein-design-skills --skill protein-qc"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/proteinmpnn", "name": "proteinmpnn", "kind": "skill", "official": false, "desc": "Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/proteinmpnn", "install": ["npx skills add adaptyvbio/protein-design-skills --skill proteinmpnn"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/protenix", "name": "protenix", "kind": "skill", "official": false, "desc": "Structure prediction with Protenix, an open AlphaFold3 reproduction. Use this skill when: (1) Predicting complex structures with an AF3-class model, (2) Wanting an open alternative to AF3 alongside Boltz and Chai, (3) Validating designed binder-target complexes. For QC thresholds, use protein-qc. For ipSAE ranking, use ipsae.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/protenix", "install": ["npx skills add adaptyvbio/protein-design-skills --skill protenix"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/rfdiffusion", "name": "rfdiffusion", "kind": "skill", "official": false, "desc": "Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying hotspot residues for interface design, (5) Creating symmetric oligomers. For sequence design after backbone generation, use proteinmpnn. For structure validation, use alphafold or ch", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/rfdiffusion", "install": ["npx skills add adaptyvbio/protein-design-skills --skill rfdiffusion"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/setup", "name": "setup", "kind": "skill", "official": false, "desc": "First-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with \"file not found\" or \"modal: command not found\", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/setup", "install": ["npx skills add adaptyvbio/protein-design-skills --skill setup"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/solublempnn", "name": "solublempnn", "kind": "skill", "official": false, "desc": "Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/solublempnn", "install": ["npx skills add adaptyvbio/protein-design-skills --skill solublempnn"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:adaptyvbio/protein-design-skills/skills/uniprot", "name": "uniprot", "kind": "skill", "official": false, "desc": "Access UniProt for protein sequence and annotation retrieval. Use this skill when: (1) Looking up protein sequences by accession, (2) Finding functional annotations, (3) Getting domain boundaries, (4) Finding homologs and variants, (5) Cross-referencing to PDB structures. For structure retrieval, use pdb. For sequence design, use proteinmpnn.", "repo": "https://github.com/adaptyvbio/protein-design-skills/tree/HEAD/skills/uniprot", "install": ["npx skills add adaptyvbio/protein-design-skills --skill uniprot"], "stars": 174, "pushed": "2026-06-11T20:17:33Z", "license": "MIT", "plat": "uniprot", "live": "Not checked (runs locally)"}, {"id": "skill:op7418/guizang-sports-skill", "name": "fit-ride-studio", "kind": "skill", "official": false, "desc": "Analyze one or more cycling, running, or hiking FIT/KML activity tracks, generate evidence-based single or aggregate reports, open the bundled local Ride Relief report page when the environment supports it, enrich results with user-confirmed public location context, and create shareable PNG or H.264 MP4 route stories on request. Use whenever a user wants to analyze, compare, visualize, or export .fit or .kml activities, including requests that mention a workout but do not yet include track files", "repo": "https://github.com/op7418/guizang-sports-skill", "install": ["npx skills add op7418/guizang-sports-skill --skill fit-ride-studio"], "stars": 153, "pushed": "2026-08-09T16:36:59Z", "license": "AGPL-3.0", "plat": "fit-ride-studio", "live": "Not checked (runs locally)"}, {"id": "skill:zengbin93/czsc_skills/skills/czsc-thinking", "name": "czsc-thinking", "kind": "skill", "official": false, "desc": "教导 AI 代理如何以缠论原文的思维方式分析交易机会。当需要分析股票买卖点、大盘行情、制定交易策略或进行市场研判时使用此技能。适用于：(1) 股票买卖点分析 (2) 大盘趋势研判 (3) 交易心理分析 (4) 操作策略制定 (5) 风险控制思考", "repo": "https://github.com/zengbin93/czsc_skills/tree/HEAD/skills/czsc-thinking", "install": ["npx skills add zengbin93/czsc_skills --skill czsc-thinking"], "stars": 146, "pushed": "2026-03-22T04:02:36Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:RConsortium/pharma-skills/gsdose", "name": "gsdose", "kind": "skill", "official": false, "desc": "Design and analyze inferentially seamless phase 2/3 adaptive trials with dose selection in the phase 2 part — multi-arm multi-stage (MAMS) group sequential designs for oncology survival endpoints — using the R package gsDose (Zhang & Jin 2025): inverse normal combination tests across pre/post dose-selection cohorts, Dunnett-based closed testing across dose arms, and alpha-spending efficacy boundaries. The skill guides the user through a structured Q&A that collects every required input one quest", "repo": "https://github.com/RConsortium/pharma-skills/tree/HEAD/gsdose", "install": ["npx skills add RConsortium/pharma-skills --skill gsdose"], "stars": 144, "pushed": "2026-11-13T17:16:45Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:karaage0703/ai-assistant-workspace/skills/xs-health-advisor", "name": "xs-health-advisor", "kind": "skill", "official": false, "desc": "食事・運動の記録と健康管理アドバイスを行うスキル。食事報告からカロリー概算、運動記録、週次レポートを生成。データはmemory/に記録。「健康チェック」「週次ヘルスレポート」「食事記録して」「運動記録して」で使用。", "repo": "https://github.com/karaage0703/ai-assistant-workspace/tree/HEAD/skills/xs-health-advisor", "install": ["npx skills add karaage0703/ai-assistant-workspace --skill xs-health-advisor"], "stars": 137, "pushed": "2026-09-23T12:12:36Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.coroslab/coros-mcp", "name": "COROS", "kind": "mcp", "official": null, "desc": "Access COROS activity, training and health data, and manage training plans and workouts.", "repo": "https://github.com/coroslab/COROS-MCP", "install": ["https://mcpus.coros.com/mcp", "https://mcpeu.coros.com/mcp", "https://mcpcn.coros.com/mcp"], "stars": 82, "pushed": "2026-10-29T03:52:29Z", "license": null, "plat": "coros", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.rwestergren/cronometer-api-mcp", "name": "io.github.rwestergren/cronometer-api-mcp", "kind": "mcp", "official": null, "desc": "MCP server for Cronometer nutrition tracking — food logs, diary, macros, fasting", "repo": "https://github.com/rwestergren/cronometer-api-mcp", "install": ["pypi:cronometer-api-mcp"], "stars": 71, "pushed": "2026-11-08T23:02:52Z", "license": "MIT", "plat": "io.github.rwestergren/cronometer-api-mcp", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Mason-Levyy/garmin-personal-mcp", "name": "Garmin Personal MCP", "kind": "mcp", "official": null, "desc": "Garmin Connect on Garmin's official Personal API: sleep, HRV, body battery, activities.", "repo": "https://github.com/Mason-Levyy/garmin-personal-mcp", "install": ["pypi:garmin-personal-mcp", "oci:ghcr.io/mason-levyy/garmin-personal-mcp:0.3.0"], "stars": 70, "pushed": "2026-11-04T05:42:30Z", "license": "MIT", "plat": "garmin connect", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.soma-intelligence/open-health-sync", "name": "Open Health Sync", "kind": "mcp", "official": null, "desc": "Read-only access to self-hosted health data: samples, sleep, workouts, nutrition, daily summaries.", "repo": "https://github.com/soma-intelligence/open-health-sync", "install": ["oci:ghcr.io/soma-intelligence/open-health-sync:0.2.0"], "stars": 66, "pushed": "2026-11-09T09:06:56Z", "license": "AGPL-3.0", "plat": "self-hosted health data", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.qrussell/wemo", "name": "io.github.qrussell/wemo", "kind": "mcp", "official": null, "desc": "Control WeMo smart home devices - discover, toggle, adjust brightness, and manage HomeKit codes", "repo": "https://github.com/qrussell/wemo-ops-center", "install": ["pypi:wemo-mcp-server"], "stars": 55, "pushed": "2026-02-25T21:34:27Z", "license": null, "plat": "wemo", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.akutishevsky/withings", "name": "Withings Health Data", "kind": "mcp", "official": null, "desc": "MCP server for Withings health data — sleep, activity, heart, and body metrics.", "repo": "https://github.com/akutishevsky/withings-mcp", "install": ["https://withings-mcp.com/mcp"], "stars": 45, "pushed": "2026-09-09T10:59:49Z", "license": "MIT", "plat": "withings", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.mitchhankins01/oura-ring-mcp", "name": "io.github.mitchhankins01/oura-ring-mcp", "kind": "mcp", "official": null, "desc": "Connect your Oura Ring to Claude AI assistants. Health metrics with smart analysis tools.", "repo": "https://github.com/mitchhankins01/oura-ring-mcp", "install": ["npm:oura-ring-mcp"], "stars": 35, "pushed": "2026-02-08T15:51:14Z", "license": null, "plat": "oura ring", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Mason-Levyy/mfp-mcp", "name": "MyFitnessPal MCP", "kind": "mcp", "official": null, "desc": "Log MyFitnessPal by talking to your AI: search foods with macros, log meals, weight, water and more.", "repo": "https://github.com/Mason-Levyy/myfitnesspal-mcp", "install": ["pypi:mfp-mcp"], "stars": 32, "pushed": "2026-11-12T23:33:06Z", "license": "MIT", "plat": "myfitnesspal", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Jezue/fitatu-api", "name": "Fitatu", "kind": "mcp", "official": null, "desc": "Unofficial Fitatu calorie counter: read and log food, weight, water and workouts.", "repo": "https://github.com/Jezue/fitatu_library", "install": ["pypi:fitatu-api"], "stars": 24, "pushed": "2026-11-11T12:44:33Z", "license": "MIT", "plat": "fitatu", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.BerkKilicoglu/google-health-fitbit-mcp", "name": "io.github.BerkKilicoglu/google-health-fitbit-mcp", "kind": "mcp", "official": null, "desc": "Privacy-first Google Health API MCP server for health, sleep, activity and HR agents", "repo": "https://github.com/BerkKilicoglu/google-health-fitbit-mcp", "install": ["npm:google-health-fitbit-mcp"], "stars": 19, "pushed": "2026-08-04T09:03:11Z", "license": "MIT", "plat": "google health", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.sepehr071/iran-gold-mcp", "name": "Iran Gold Prices", "kind": "mcp", "official": null, "desc": "Read-only 18k gold prices on Iranian sites: Milli.gold, Wallgold, Melligold, Daric, Taline, Talano.", "repo": "https://github.com/sepehr071/iran-gold-mcp", "install": ["pypi:iran-gold-mcp"], "stars": 18, "pushed": "2026-11-08T05:58:25Z", "license": "MIT", "plat": "milli.gold", "live": "Not checked (runs locally)"}, {"id": "mcp:com.synectus.opengmb/server", "name": "OpenGMB", "kind": "mcp", "official": null, "desc": "Manage Google Business Profiles: reviews, posts, hours and rankings, with human approval.", "repo": "https://github.com/synectus/OpenGMB", "install": ["https://opengmb.synectus.com/mcp"], "stars": 18, "pushed": "2026-10-29T08:48:05Z", "license": "MIT", "plat": "medlineplus", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.yuridivonis/whoop-mcp-server", "name": "Whoop MCP Server", "kind": "mcp", "official": null, "desc": "Your WHOOP recovery, sleep, strain and workouts in Claude, ChatGPT or other MCP apps. Self-hosted.", "repo": "https://github.com/yuridivonis/whoop-mcp-server", "install": ["oci:ghcr.io/yuridivonis/whoop-mcp-server:1.5.1"], "stars": 18, "pushed": "2026-11-04T05:39:45Z", "license": "MIT", "plat": "whoop", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.ContentWriterco/kolejki-nfz-mcp", "name": "io.github.ContentWriterco/kolejki-nfz-mcp", "kind": "mcp", "official": null, "desc": "NFZ waiting times in Poland: where a specialist, test or treatment is available soonest", "repo": "https://github.com/ContentWriterco/kolejki-nfz-mcp", "install": ["npm:kolejki-nfz-mcp", "https://kolejki.compabase.com/mcp"], "stars": 15, "pushed": "2026-10-24T10:50:54Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.rwestergren/inbody-api-mcp", "name": "io.github.rwestergren/inbody-api-mcp", "kind": "mcp", "official": null, "desc": "MCP server for InBody body-composition data — scans, body fat, muscle mass, body water", "repo": "https://github.com/rwestergren/inbody-api-mcp", "install": ["pypi:inbody-api-mcp"], "stars": 15, "pushed": "2026-06-19T16:34:14Z", "license": "MIT", "plat": "io.github.rwestergren/inbody-api-mcp", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Exekiel179/mne-mcp", "name": "MNE-MCP", "kind": "mcp", "official": null, "desc": "MNE-Python neurophysiology analysis (EEG, MEG, sEEG, ECoG, fNIRS) via the Model Context Protocol", "repo": "https://github.com/Exekiel179/MNE-MCP", "install": ["pypi:mne-mcp"], "stars": 12, "pushed": "2026-10-14T10:10:52Z", "license": "MIT", "plat": "mne-python", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.DrBradStanfield/health-by-dr-brad", "name": "Health by Dr Brad", "kind": "mcp", "official": null, "desc": "Read and add to your own health record, kept in your own cloud, and get a health plan from it.", "repo": "https://github.com/DrBradStanfield/roadmap", "install": ["https://mcp.drstanfield.com/mcp"], "stars": 11, "pushed": "2026-11-13T07:01:46Z", "license": "MIT", "plat": "", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.PhilipAD/health-export-mcp", "name": "MetricBridge Apple Health", "kind": "mcp", "official": null, "desc": "Ask your agent about your own Apple Health data: 194 metrics, 21 tools, local, 0 deps.", "repo": "https://github.com/PhilipAD/health-export-mcp", "install": ["npm:health-export-mcp", "mcpb:https://github.com/PhilipAD/health-export-mcp/releases/download/v1.11.0/health-export.mcpb"], "stars": 11, "pushed": "2026-11-03T16:50:36Z", "license": "MIT", "plat": "apple health", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.pardesline/pardesline", "name": "pardesline", "kind": "mcp", "official": null, "desc": "3D vision and medical imaging: 43 datasets with licences, 73 algorithms on clouds, meshes, CT/MRI", "repo": "https://github.com/pardesline/pardesline", "install": ["pypi:pardesline"], "stars": 10, "pushed": "2026-11-04T07:55:11Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.fmp-projects/fitness-ai-connector", "name": "Fitness AI Connector", "kind": "mcp", "official": null, "desc": "Garmin data in Claude & ChatGPT via the Garmin Health API. OAuth sign-in, no password sharing.", "repo": "https://github.com/fmp-projects/fitness-ai-connector", "install": ["https://garmin-mcp-nuqd.onrender.com/mcp"], "stars": 9, "pushed": "2026-11-08T00:47:21Z", "license": "MIT", "plat": "garmin", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:ai.com.mcp/strava", "name": "HAPI Strava MCP Server", "kind": "mcp", "official": null, "desc": "Strava MCP tools for AI: athletes, activities, segments, clubs, routes. Powered by HAPI MCP server.", "repo": "https://github.com/la-rebelion/hapimcp", "install": ["https://strava.run.mcp.com.ai/mcp"], "stars": 9, "pushed": "2026-08-24T00:44:51Z", "license": "MIT", "plat": "strava", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.edxleon/runspector", "name": "Runspector — AI running coach for Garmin", "kind": "mcp", "official": null, "desc": "AI running coach for Garmin: HRV, readiness and VO2max explained, workouts on your watch", "repo": "https://github.com/edxleon/runspector", "install": ["pypi:runspector"], "stars": 9, "pushed": "2026-11-11T08:22:34Z", "license": "MIT", "plat": "garmin", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.dengxuhui/igpsport-mcp", "name": "io.github.dengxuhui/igpsport-mcp", "kind": "mcp", "official": null, "desc": "iGPSport cycling data with training metrics (NP/IF/TSS/CTL/ATL/TSB) and workout generation", "repo": "https://github.com/dengxuhui/igpsport-mcp", "install": ["pypi:igpsport-mcp"], "stars": 8, "pushed": "2026-06-30T10:25:04Z", "license": "MIT", "plat": "igpsport", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.lexthirteen-ai/orinyx-mcp", "name": "Orinyx", "kind": "mcp", "official": null, "desc": "Test healthcare AI agents' medication claims against cited FDA label text. Not for clinical use.", "repo": "https://github.com/lexthirteen-ai/orinyx-mcp", "install": ["https://orinyx-mcp.fly.dev/mcp"], "stars": 8, "pushed": "2026-11-11T22:14:01Z", "license": "Apache-2.0", "plat": "fda", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.Thecimal/quantified-self-mcp", "name": "Quantified Self MCP", "kind": "mcp", "official": null, "desc": "Local-first MCP server for personal health data with privacy, statistical evidence, and provenance.", "repo": "https://github.com/Thecimal/quantified-self-mcp", "install": ["pypi:quantified-self-mcp"], "stars": 8, "pushed": "2026-11-13T20:46:32Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.DynamicsNinja/garminconnect", "name": "Garmin Connect", "kind": "mcp", "official": null, "desc": "Unofficial Garmin Connect access: sleep, HRV, training, activities, workouts and weight.", "repo": "https://github.com/DynamicsNinja/garminconnect-js", "install": ["https://garmin.ficdev.xyz/mcp"], "stars": 6, "pushed": "2026-11-12T09:11:31Z", "license": "MIT", "plat": "garmin connect", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.emirmrkaljevic/iotaudit", "name": "io.github.emirmrkaljevic/iotaudit", "kind": "mcp", "official": null, "desc": "Audit your own smart-home devices: destinations, encryption, open ports, A-F grade.", "repo": "https://github.com/emirmrkaljevic/iotaudit", "install": ["pypi:iotaudit"], "stars": 6, "pushed": "2026-11-12T09:13:57Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.QWarranto/leafengines", "name": "io.github.QWarranto/leafengines", "kind": "mcp", "official": null, "desc": "🌱 Agricultural AI: Soil analysis, crop recommendations, weather forecasts. FREE TurboQuant.", "repo": "https://github.com/QWarranto/leafengines-claude-mcp", "install": ["npm:@ancientwhispers54/leafengines-mcp-server"], "stars": 6, "pushed": "2026-07-12T21:29:49Z", "license": "MIT", "plat": "agricultural ai", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.atomchung/long-run-hybrid-coach", "name": "Long Run Hybrid Coach", "kind": "mcp", "official": null, "desc": "Adaptive running and strength coaching for the long run.", "repo": "https://github.com/atomchung/long-run-hybrid-coach", "install": ["https://mcp.paceandstaystrong.com/mcp"], "stars": 6, "pushed": "2026-11-13T12:57:29Z", "license": "MIT", "plat": "running coaching", "live": "Responds, sign-in needed (checked 2026-11-13)"}, {"id": "mcp:io.github.martc03/public-health", "name": "Public Health", "kind": "mcp", "official": null, "desc": "NIH clinical trials and FDA adverse event reports. 4 MCP tools for health research.", "repo": "https://github.com/martc03/gov-mcp-servers", "install": ["https://public-health-mcp.apify.actor/mcp"], "stars": 6, "pushed": "2026-04-06T07:06:08Z", "license": "MIT", "plat": "nih", "live": "Down · HTTP 404 (checked 2026-11-13)"}, {"id": "plugin:anthropics/claude-plugins-official:boltz", "name": "boltz", "kind": "claude-plugin", "official": null, "desc": "Predict structures, screen molecules and proteins, and design binders with Boltz from Claude Code.", "repo": "https://github.com/boltz-bio/boltz-api-skills.git", "install": ["claude plugin install boltz@claude-plugins-official"], "stars": 5, "pushed": "2026-10-25T01:45:57Z", "license": "MIT", "plat": "boltz", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.NyxToolsDev/dicom-hl7-mcp-server", "name": "DICOM/HL7/FHIR Interoperability MCP Server", "kind": "mcp", "official": null, "desc": "Bridges DICOM, HL7v2, and FHIR with cross-standard mapping and optional PACS connectivity.", "repo": "https://github.com/NyxToolsDev/dicom-hl7-mcp-server", "install": ["pypi:dicom-hl7-mcp"], "stars": 5, "pushed": "2026-07-29T18:38:12Z", "license": "MIT", "plat": "dicom/hl7v2/fhir", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.rewire-bio/genomics-mcp", "name": "Genomics MCP", "kind": "mcp", "official": null, "desc": "Bounded genomic data from archives and indexed files, with versioned reference evidence.", "repo": "https://github.com/rewire-bio/genomics-mcp", "install": ["oci:ghcr.io/rewire-bio/genomics-mcp:0.1.0", "mcpb:https://github.com/rewire-bio/genomics-mcp/releases/download/v0.1.0/genomics-mcp-0.1.0.mcpb"], "stars": 5, "pushed": "2026-10-02T09:20:28Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.cyanheads/astronomy-mcp-server", "name": "io.github.cyanheads/astronomy-mcp-server", "kind": "mcp", "official": null, "desc": "Offline observational astronomy: positions, rise/set, moon phases, eclipses, and seasons.", "repo": "https://github.com/cyanheads/astronomy-mcp-server", "install": ["npm:@cyanheads/astronomy-mcp-server", "npm:@cyanheads/astronomy-mcp-server", "https://astronomy.caseyjhand.com/mcp"], "stars": 5, "pushed": "2026-10-28T18:06:15Z", "license": "Apache-2.0", "plat": "astronomy", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.cyanheads/cdc-health-mcp-server", "name": "io.github.cyanheads/cdc-health-mcp-server", "kind": "mcp", "official": null, "desc": "Search and query CDC public health data — mortality, vaccinations, surveillance, behavioral risk.", "repo": "https://github.com/cyanheads/cdc-health-mcp-server", "install": ["npm:@cyanheads/cdc-health-mcp-server", "npm:@cyanheads/cdc-health-mcp-server", "https://cdc.caseyjhand.com/mcp"], "stars": 5, "pushed": "2026-11-01T09:21:25Z", "license": "Apache-2.0", "plat": "cdc", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.athril/agentic-target-evidence", "name": "Agentic Target Evidence", "kind": "mcp", "official": null, "desc": "Read-only biomedical evidence tools for drug-target validation: DepMap, gnomAD, OpenTargets, PubMed…", "repo": "https://github.com/athril/agentic-target-evidence", "install": ["oci:ghcr.io/athril/agentic-target-evidence/mcp-gateway:v0.1.5"], "stars": 4, "pushed": "2026-11-05T22:25:34Z", "license": "Apache-2.0", "plat": "biomedical evidence", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.unibio-intelligence/biomedmcp", "name": "biomedmcp", "kind": "mcp", "official": null, "desc": "Integrated public biomedical research with cited, bounded evidence.", "repo": "https://github.com/unibio-intelligence/biomedmcp", "install": ["pypi:biomedmcp", "oci:ghcr.io/unibio-intelligence/biomedmcp:0.2.6", "https://biomedmcp.unibiointelligence.com/mcp"], "stars": 4, "pushed": "2026-11-08T23:18:27Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.MoleCare/molecare-mcp", "name": "io.github.MoleCare/molecare-mcp", "kind": "mcp", "official": null, "desc": "Educational dermatology knowledge and mole tracking. No credentials needed. Not a medical device.", "repo": "https://github.com/MoleCare/molecare-mcp", "install": ["npm:molecare-mcp"], "stars": 4, "pushed": "2026-11-07T09:03:45Z", "license": "Apache-2.0", "plat": "dermatology", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.csjoblom/musclesworked", "name": "MusclesWorked", "kind": "mcp", "official": null, "desc": "Exercise-to-muscle mapping: 856 exercises, 65 muscles, workout analysis & alternatives.", "repo": "https://github.com/csjoblom/musclesworked-mcp", "install": ["npm:musclesworked-mcp"], "stars": 4, "pushed": "2026-02-17T17:30:23Z", "license": "MIT", "plat": "exercise", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.sepehr071/paziresh24-mcp", "name": "Paziresh24", "kind": "mcp", "official": null, "desc": "Unofficial read-only Paziresh24: find Iranian doctors, profiles, prices, reviews and free slots.", "repo": "https://github.com/sepehr071/paziresh24-mcp", "install": ["pypi:paziresh24-mcp"], "stars": 4, "pushed": "2026-11-08T05:44:45Z", "license": "MIT", "plat": "paziresh24", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.luisbedoia/thermoprops", "name": "Thermoprops", "kind": "mcp", "official": null, "desc": "Thermodynamic properties and cycles with CoolProp: fluid states, refrigeration, Rankine, Brayton.", "repo": "https://github.com/luisbedoia/thermoprops", "install": ["https://thermoprops-api.luisbedoia.workers.dev/mcp"], "stars": 4, "pushed": "2026-11-12T11:42:51Z", "license": "MIT", "plat": "", "live": "Live · 2301 ms · 3 tools (checked 2026-11-13)"}, {"id": "mcp:io.github.forever-healthy/evipedia-mcp", "name": "Evipedia", "kind": "mcp", "official": null, "desc": "MCP Server for evipedia.ai — evidence-based reviews of health & longevity interventions", "repo": "https://github.com/forever-healthy/evipedia-mcp", "install": ["npm:evipedia-mcp"], "stars": 3, "pushed": "2026-09-09T12:49:08Z", "license": "MIT", "plat": "evipedia.ai", "live": "Not checked (runs locally)"}, {"id": 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`fitbit_get_devices` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.github.almostjacked/fitness-tools", "name": "Fitness Tools", "kind": "mcp", "official": null, "desc": "Deterministic fitness calculators — TDEE, adaptive TDEE, body fat, 1RM, macros — with consensus.", "repo": "https://github.com/almostjacked/fitness-tools", "install": ["npm:@almostjacked/fitness-tools-mcp", "https://fitness-tools-mcp.ajwallacemusic.workers.dev/mcp"], "stars": 1, "pushed": "2026-07-24T16:57:13Z", "license": "MIT", "plat": "fitness tools", "live": "Not checked (runs locally)"}, {"id": "mcp:io.usefulapi/halaxy", "name": "Halaxy MCP by usefulapi", "kind": "mcp", "official": null, "desc": "Read Halaxy patients, appointments, slots and invoices; create patients, appointments and notes.", "repo": "https://github.com/m190/usefulapi-mcp", "install": ["https://halaxy.usefulapi.io/mcp"], "stars": 1, "pushed": "2026-11-13T06:39:36Z", "license": "MIT", "plat": "halaxy", "live": "Live · 620 ms · 35 tools · `halaxy_list_fees` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.github.k0nnect/harald", "name": "harald", "kind": "mcp", "official": null, "desc": "helps coding agents work out the bluetooth le and classic protocol of gadgets you own", "repo": "https://github.com/k0nnect/harald", "install": ["pypi:harald"], "stars": 1, "pushed": "2026-11-02T23:06:01Z", "license": "MIT", "plat": "bluetooth", "live": "Not checked (runs locally)"}, {"id": "mcp:io.usefulapi/health-gorilla", "name": "Health Gorilla MCP by usefulapi", "kind": "mcp", "official": null, "desc": "Query Health Gorilla FHIR patients, conditions, medications and lab results.", "repo": "https://github.com/m190/usefulapi-mcp", "install": ["https://health-gorilla.usefulapi.io/mcp"], "stars": 1, "pushed": "2026-11-13T06:39:36Z", "license": "MIT", "plat": "health gorilla", "live": "Live · 569 ms · 16 tools · `healthgorilla_fhir_search` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.usefulapi/healthie", "name": "Healthie MCP by usefulapi", "kind": "mcp", "official": null, "desc": "Hosted MCP server for the Healthie EHR & telehealth API: patients, appointments, charting, tasks.", "repo": "https://github.com/m190/usefulapi-mcp", "install": ["https://healthie.usefulapi.io/mcp"], "stars": 1, "pushed": "2026-11-13T06:39:36Z", "license": "MIT", "plat": "healthie", "live": "Live · 531 ms · 16 tools · `healthie_list_forms` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.usefulapi/hint-health", "name": "Hint Health MCP by usefulapi", "kind": "mcp", "official": null, "desc": "Read Hint Health patients, memberships, plans, invoices and payments for direct primary care.", "repo": "https://github.com/m190/usefulapi-mcp", "install": ["https://hint-health.usefulapi.io/mcp"], "stars": 1, "pushed": "2026-11-13T06:39:36Z", "license": "MIT", "plat": "hint health", "live": "Live · 526 ms · 17 tools · `hinthealth_list_plans` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.github.Imgrund/hybridlog", "name": "hybridlog", "kind": "mcp", "official": null, "desc": "Reads your own Garmin mirror: readiness, training load, muscle freshness, and read-only SQL.", "repo": "https://github.com/Imgrund/hybridlog", "install": ["oci:ghcr.io/imgrund/hybridlog:0.1.19", "https://{hybridlog_host}/mcp/garmin"], "stars": 1, "pushed": "2026-11-12T11:23:03Z", "license": "MIT", "plat": "garmin", "live": "Not checked (runs locally)"}, {"id": "mcp:io.usefulapi/infermedica", "name": "Infermedica MCP by usefulapi", "kind": "mcp", "official": null, "desc": "Run Infermedica symptom checks, diagnosis, triage and condition lookups.", "repo": "https://github.com/m190/usefulapi-mcp", "install": ["https://infermedica.usefulapi.io/mcp"], "stars": 1, "pushed": "2026-11-13T06:39:36Z", "license": "MIT", "plat": "infermedica", "live": "Live · 529 ms · 17 tools · `infermedica_get_info` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:io.github.chrischall/myatriumhealth-mcp", "name": "io.github.chrischall/myatriumhealth-mcp", "kind": "mcp", "official": null, "desc": "MyAtriumHealth (Epic MyChart) records via your signed-in browser tab", "repo": "https://github.com/chrischall/myatriumhealth-mcp", "install": ["npm:@chrischall/myatriumhealth-mcp"], "stars": 1, "pushed": "2026-11-12T06:36:06Z", "license": "MIT", "plat": "myatriumhealth", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.CSOAI-ORG/pet-care-ai-mcp", "name": "io.github.CSOAI-ORG/pet-care-ai-mcp", "kind": "mcp", "official": null, "desc": "Pet Care Ai MCP Server by MEOK AI Labs", "repo": "https://github.com/CSOAI-ORG/pet-care-ai-mcp", "install": ["pypi:pet-care-ai-mcp"], "stars": 1, "pushed": "2026-11-13T10:00:41Z", "license": "MIT", "plat": "meok ai labs", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.cyanheads/pharmacogenomics-guidelines-mcp-server", "name": "io.github.cyanheads/pharmacogenomics-guidelines-mcp-server", 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Not advice.", "repo": "https://github.com/jbobbink/zorgverzekering-mcp", "install": ["https://mcp.zorgverzekering.chat/mcp"], "stars": 0, "pushed": "2026-11-11T10:35:06Z", "license": null, "plat": "zorgverzekering.chat", "live": "Live · 2168 ms · 12 tools · free to try: `get_intake_schema` answered without a key (checked 2026-11-13)"}, {"id": "mcp:il.co.bituchon/car-insurance", "name": "ביטוחון: Israeli car data and car-insurance facts", "kind": "mcp", "official": null, "desc": "Israeli car data by plate or model, licence fee, recalls, thefts and car-insurance facts.", "repo": null, "install": ["https://bituchon.co.il/api/mcp"], "stars": null, "pushed": null, "license": null, "plat": "", "live": "Live · 3036 ms · 12 tools · free to try: `model_info` answered without a key (checked 2026-11-13)"}, {"id": "mcp:com.agentzukan/japan-medical-facilities", "name": "エージェント図鑑（医療・介護・保育）", "kind": "mcp", "official": null, "desc": "全国の医療機関224,540件・介護事業所189,943件・保育施設（東京・大阪パイロット）を検索できるMCPサーバー。地点と半径による商圏集計にも対応。", "repo": null, "install": ["https://agentzukan.com/mcp"], "stars": null, "pushed": null, "license": null, "plat": "", "live": "Live · 3668 ms · 10 tools (checked 2026-11-13)"}, {"id": "mcp:com.kubiaka-now/mcp", "name": "クビアカのいま (Aromia bungii in Japan)", "kind": "mcp", "official": null, "desc": "Red-necked longhorn beetle (Aromia bungii) in Japan: damage status, new detections, rewards by city.", "repo": null, "install": ["https://kubiaka-now.com/mcp"], "stars": null, "pushed": null, "license": null, "plat": "aromia bungii", "live": "Live · 674 ms · 5 tools · `get_changes_since` asked for payment (checked 2026-11-13)"}, {"id": "mcp:com.penguindriver/twostroke", "name": "二行程經典車資料庫", "kind": "mcp", "official": null, "desc": "二行程經典車資料庫：經典二行程機車規格、零件與保養。台灣繁體中文 MCP 工具。", "repo": null, "install": ["https://twostroke.penguindriver.com/mcp"], "stars": null, "pushed": null, "license": null, "plat": "classic two-stroke motorcycles", "live": "Live · 1172 ms · 4 tools · free to try: `get_maintenance` answered without a key (checked 2026-11-13)"}, {"id": "mcp:com.penguindriver/health", "name": "台灣健康資訊", "kind": "mcp", "official": null, "desc": "台灣健康資訊：篩檢、慢性病與飲食等健康資訊整理。台灣繁體中文 MCP 工具。", "repo": null, "install": ["https://health.penguindriver.com/mcp"], "stars": null, "pushed": null, "license": null, "plat": "", "live": "Live · 12525 ms · 10 tools · free to try: `check_product_safety` answered without a key (checked 2026-11-13)"}, {"id": "mcp:com.penguindriver/wellness", "name": "數位健康與遠距醫療指南", "kind": "mcp", "official": null, "desc": "數位健康與遠距醫療指南：視訊看診、心情自評與睡眠放鬆。台灣繁體中文 MCP 工具。", "repo": null, "install": ["https://wellness.penguindriver.com/mcp"], "stars": null, "pushed": null, "license": null, "plat": "digital health and telemedicine", "live": "Live · 510 ms · 8 tools · free to try: `check_telehealth_eligibility` answered without a key (checked 2026-11-13)"}, {"id": "mcp:io.github.h2-papers/mcp-server", "name": "水素分子医学文献データベース — Molecular hydrogen medical literature (H2 Papers)", "kind": "mcp", "official": null, "desc": "PubMed molecular-hydrogen medical literature with delivery-aware safety notes inline.", "repo": "https://github.com/h2-papers/h2-papers-mcp-server", "install": ["npm:@h2-papers/mcp-server", "https://h2-papers.org/mcp"], "stars": 0, "pushed": "2026-05-15T07:29:17Z", "license": "MIT", "plat": "pubmed", "live": "Not checked (runs locally)"}, {"id": "mcp:tw.meang/meang", "name": "覓安居", "kind": "mcp", "official": null, "desc": "Search Taiwan long-term care facilities; signed-in users get profile and notes.", "repo": null, "install": ["https://meang.tw/mcp"], "stars": null, "pushed": null, "license": null, "plat": "", "live": "Live · 902 ms · 9 tools · `list_notes` asked for an API key (checked 2026-11-13)"}, {"id": "mcp:com.threebillions.yunseul/saju", "name": "윤슬 Yunseul — 사주 만세력", "kind": "mcp", "official": null, "desc": "Korean Saju (Four Pillars) calculator: birth chart, day pillar, luck flow, monthly fortune", "repo": null, "install": ["https://yunseul.threebillions.com/api/agent/mcp"], "stars": null, "pushed": null, "license": null, "plat": "윤슬 yunseul — 사주 만세력", "live": "Live · 3242 ms · 6 tools · free to try: `list_prices` answered without a key (checked 2026-11-13)"}]