[{"id": "skill:K-Dense-AI/scientific-agent-skills/skills/aeon", "name": "aeon", "kind": "skill", "official": false, "desc": "This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/aeon", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill aeon"], "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/anndata", "name": "anndata", "kind": "skill", "official": false, "desc": "Handles annotated matrices in single-cell analysis, .h5ad and Zarr files, and integration with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/anndata", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill anndata"], "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/arbor", "name": "arbor", "kind": "skill", "official": false, "desc": "Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor CLI.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/arbor", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill arbor"], "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/arboreto", "name": "arboreto", "kind": "skill", "official": false, "desc": "Infers candidate gene regulatory networks from bulk or single-cell expression data using AertsLab Arboreto GRNBoost2 and GENIE3. Use for transcription factor-target association ranking, compatible Dask execution, sparse expression inputs, and network stability checks.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/arboreto", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill arboreto"], "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/astropy", "name": "astropy", "kind": "skill", "official": false, "desc": "Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/astropy", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill astropy"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "astropy", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/benchling-integration", "name": "benchling-integration", "kind": "skill", "official": false, "desc": "Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/benchling-integration", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill benchling-integration"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "benchling", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/bids", "name": "bids", "kind": "skill", "official": false, "desc": "Organizes, queries, validates, and converts Brain Imaging Data Structure (BIDS) datasets. Supports organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/bids", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill bids"], "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/biopython", "name": "biopython", "kind": "skill", "official": false, "desc": "Provides Biopython workflows for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Supports batch processing, custom molecular-biology pipelines, BLAST automation, structure analysis, and motif analysis.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/biopython", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill biopython"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "biopython", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/bioservices", "name": "bioservices", "kind": "skill", "official": false, "desc": "Provides a Python interface to bioinformatics services including UniProt, KEGG, ChEMBL, Reactome, QuickGO, and UniChem. Used for cross-database protein annotation, pathway retrieval, chemical identifier mapping, and integrated biological data workflows with BioServices.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/bioservices", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill bioservices"], "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/bulk-rnaseq", "name": "bulk-rnaseq", "kind": "skill", "official": false, "desc": "Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/bulk-rnaseq", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill bulk-rnaseq"], "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/cantera", "name": "cantera", "kind": "skill", "official": false, "desc": "Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement. Use for combustion kinetics, closed adiabatic ideal-gas constant-volume or constant-pressure ignition, temperature histories, or mechanism-specific ignition-delay comparisons.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/cantera", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill cantera"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "cantera", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/cellprofiler", "name": "cellprofiler", "kind": "skill", "official": false, "desc": "Runs reproducible CellProfiler microscopy pipelines for nuclear segmentation, cell counts, and per-object fluorescence measurements. Supports image/channel manifests, headless batch execution, segmentation overlays, and measurement QC for 2D fluorescence assays.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/cellprofiler", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill cellprofiler"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "cellprofiler", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/cellxgene-census", "name": "cellxgene-census", "kind": "skill", "official": false, "desc": "Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/cellxgene-census", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill cellxgene-census"], "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/cirq", "name": "cirq", "kind": "skill", "official": false, "desc": "Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/cirq", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill cirq"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "google quantum ai", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/cobrapy", "name": "cobrapy", "kind": "skill", "official": false, "desc": "Performs constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/cobrapy", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill cobrapy"], "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/openpiv", "name": "openpiv", "kind": "skill", "official": false, "desc": "Performs Particle Image Velocimetry (PIV) analysis with OpenPIV. Use when extracting velocity fields from PIV image pairs, analyzing fluid dynamics or flow visualization experiments, cross-correlating interrogation windows, validating and replacing spurious PIV vectors, or computing vorticity, strain rate, and turbulence statistics from measured velocity fields.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/openpiv", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill openpiv"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "openpiv", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/pathml", "name": "pathml", "kind": "skill", "official": false, "desc": "Supports local computational pathology research with PathML: slide loading and tiling, preprocessing and QC, h5path storage, multiplex quantification, spatial graphs, and bounded model inference. Use for whole-slide H&E, CODEX, Vectra, Mesmer, HoVer-Net, and HACTNet workflows.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pathml", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pathml"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "pathml", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/pathway-enrichment", "name": "pathway-enrichment", "kind": "skill", "official": false, "desc": "Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), ", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pathway-enrichment", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pathway-enrichment"], "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/phylogenetics", "name": "phylogenetics", "kind": "skill", "official": false, "desc": "Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein families, and cautiously interpreted dated phylogenies.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/phylogenetics", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill phylogenetics"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "mafft/iq-tree", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/pkpd-modeling", "name": "pkpd-modeling", "kind": "skill", "official": false, "desc": "Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include \"pharmacokinetics\", \"pharmacodynamics\", \"PK/PD\",", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pkpd-modeling", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pkpd-modeling"], "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/primer-design", "name": "primer-design", "kind": "skill", "official": false, "desc": "Designs and audits PCR and RT-qPCR primers with Primer3, explicit thermodynamic conditions, reference-based off-target amplification searches, and traceable sequence coordinates. Use for designing primer pairs, checking existing primers, exon-junction or isoform-specific assays, variant masking, cloning tails, multiplex compatibility, and interpreting Primer-BLAST results. Includes bounded local in-silico PCR and BLAST screening; distinguishes computational candidates from experimentally validat", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/primer-design", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill primer-design"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "primer3", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/pybamm", "name": "pybamm", "kind": "skill", "official": false, "desc": "Simulates lithium-ion battery charge, discharge and rest experiments with PyBaMM, records parameter-set provenance, checks mesh and solver sensitivity, and compares predicted voltage curves with measured cycling data. Use for SPM or DFN electrochemical battery modeling, C-rate protocols, voltage cutoffs, parameter studies and numerical validation of battery simulations.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pybamm", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pybamm"], "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/pycalphad", "name": "pycalphad", "kind": "skill", "official": false, "desc": "Computes finite-temperature CALPHAD equilibria, phase fractions, and phase compositions from thermodynamic TDB databases using pycalphad. Use for alloy phase stability, equilibrium temperature sweeps, tie lines, lever-rule checks, or reproducible phase-fraction calculations with explicit components and mole-fraction conditions.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pycalphad", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pycalphad"], "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/pydeseq2", "name": "pydeseq2", "kind": "skill", "official": false, "desc": "Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pydeseq2", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pydeseq2"], "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/pylabrobot", "name": "pylabrobot", "kind": "skill", "official": false, "desc": "Develops and reviews PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Supports PyLabRobot protocols and API questions; keep physical execution behind an explicit operator safety gate.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pylabrobot", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pylabrobot"], "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/pymatgen", "name": "pymatgen", "kind": "skill", "official": false, "desc": "Analyzes, validates, converts, and transforms materials structures and computed materials data with pymatgen. Use for local phase diagrams, symmetry sensitivity, electronic-structure I/O, and bounded Materials Project queries.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pymatgen", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pymatgen"], "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/pyopenms", "name": "pyopenms", "kind": "skill", "official": false, "desc": "Processes mass spectrometry data with pyOpenMS. Supports proteomics and metabolomics workflows—feature detection, peptide/protein identification, label-free quantification, adduct/accurate-mass annotation, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. For simple spectral comparison and small-molecule library matching use matchms.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pyopenms", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pyopenms"], "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/pysam", "name": "pysam", "kind": "skill", "official": false, "desc": "Provides Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pysam", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pysam"], "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/pytdc", "name": "pytdc", "kind": "skill", "official": false, "desc": "Provides Therapeutics Data Commons workflows through PyTDC for registry discovery, dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle scoring. Use when working with TDC therapeutic ML datasets or benchmarks.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/pytdc", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill pytdc"], "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/qiime2-amplicon", "name": "qiime2-amplicon", "kind": "skill", "official": false, "desc": "Processes paired-end 16S amplicon reads into QIIME 2 ASVs and taxonomy with retained artifact provenance. Checks paired FASTQ manifests, primer orientation diagnostics, predicted post-trimming overlap, sample IDs, runtime versions and read retention, and guides selection of compatible taxonomic classifiers.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/qiime2-amplicon", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill qiime2-amplicon"], "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/qutip", "name": "qutip", "kind": "skill", "official": false, "desc": "Simulate and audit closed and open quantum-system models with QuTiP 5, including deterministic, trajectory, steady-state, spectral, and phase-space workflows. Use for local quantum-dynamics work where physical assumptions, dimensions, and numerical convergence must be explicit.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/qutip", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill qutip"], "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/rdkit", "name": "rdkit", "kind": "skill", "official": false, "desc": "Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/rdkit", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill rdkit"], "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/relion", "name": "relion", "kind": "skill", "official": false, "desc": "Validates and executes RELION single-particle cryo-EM refinement and half-map postprocessing. Supports STAR optics/acquisition checks, particle-stack consistency, gold-standard half sets, soft-mask validation, diagnostic Fourier shell correlation, and restart guidance.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/relion", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill relion"], "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/relsa-severity-assessment", "name": "relsa-severity-assessment", "kind": "skill", "official": false, "desc": "Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a ", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/relsa-severity-assessment", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill relsa-severity-assessment"], "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/rowan", "name": "rowan", "kind": "skill", "official": false, "desc": "Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU in", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/rowan", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill rowan"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "rowan", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/scanpy", "name": "scanpy", "kind": "skill", "official": false, "desc": "Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or SingleCellExperiment RDS conversion to h5ad. Applies to established exploratory scRNA-seq workflows with explicit count and expression provenance; complementary skills cover scvi-tools models and AnnData format details.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/scanpy", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill scanpy"], "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/scikit-bio", "name": "scikit-bio", "kind": "skill", "official": false, "desc": "Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/scikit-bio", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-bio"], "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/scikit-survival", "name": "scikit-survival", "kind": "skill", "official": false, "desc": "Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/scikit-survival", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill scikit-survival"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "scikit-survival", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/scvelo", "name": "scvelo", "kind": "skill", "official": false, "desc": "Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/scvelo", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill scvelo"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "scvelo", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/scvi-tools", "name": "scvi-tools", "kind": "skill", "official": false, "desc": "Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/scvi-tools", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill scvi-tools"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "scvi-tools", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/tamarind", "name": "tamarind", "kind": "skill", "official": false, "desc": "Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tamarind", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill tamarind"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "tamarind bio", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/tellurium", "name": "tellurium", "kind": "skill", "official": false, "desc": "Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tellurium", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill tellurium"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "tellurium", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/tiledbvcf", "name": "tiledbvcf", "kind": "skill", "official": false, "desc": "Stores and retrieves genomic variant calls with TileDB-VCF. Use for indexed single-sample VCF/BCF ingestion, incremental cohorts, region and sample queries, streaming results, allele statistics, QC, and VCF/BCF export locally or through TileDB Cloud.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/tiledbvcf", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill tiledbvcf"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "tiledb-vcf", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/torchdrug", "name": "torchdrug", "kind": "skill", "official": false, "desc": "Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/torchdrug", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill torchdrug"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "torchdrug", "live": "Not checked (runs locally)"}, {"id": "skill:K-Dense-AI/scientific-agent-skills/skills/uncertainty-and-units", "name": "uncertainty-and-units", "kind": "skill", "official": false, "desc": "Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibili", "repo": "https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/uncertainty-and-units", "install": ["npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units"], "stars": 57090, "pushed": "2026-11-09T09:17:48Z", "license": "MIT", "plat": "pint", "live": "Not checked (runs locally)"}, {"id": "plugin:anthropics/claude-plugins-official:math-proof", "name": "math-proof", "kind": "claude-plugin", "official": null, "desc": "Two skills for hard mathematics problems, each ending in a self-contained proof.md: /math-proof:solo has the session work the problem itself in stages with a notes file; /math-proof:siege works on it for hours in rounds of judge and worker sub-agents and says plainly what is and is not proved.", "repo": "https://github.com/anthropics/claude-plugins-official", "install": ["claude plugin install math-proof@claude-plugins-official"], "stars": 47064, "pushed": "2026-11-13T01:14:19Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:anthropics/claude-plugins-official/plugins/claude-for-manufacturing/skills/root-cause-capa", "name": "root-cause-capa", "kind": "skill", "official": true, "desc": "Reads sensor history, inspection measurements, maintenance records and prior tickets onto one verified timeline, runs the pattern checks in code rather than by eye — SPC drift, clustering by lot, shift, operator, machine and tool change, correlation with a maintenance event — then proposes ranked candidate causal chains, each with the evidence for it, the evidence against it, and the test that would confirm or kill it. Drafts the 8D. Where the leading candidate points at purchased material, rais", "repo": "https://github.com/anthropics/claude-plugins-official/tree/HEAD/plugins/claude-for-manufacturing/skills/root-cause-capa", "install": ["npx skills add anthropics/claude-plugins-official --skill root-cause-capa"], "stars": 47064, "pushed": "2026-11-13T01:14:19Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:brycewang-stanford/Auto-Empirical-Research-Skills/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/theory/general-equilibrium-model-builder", "name": "general-equilibrium-model-builder", "kind": "skill", "official": false, "desc": "Build and solve Walrasian general equilibrium models with theory derivations and Julia computation", "repo": "https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/HEAD/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/theory/general-equilibrium-model-builder", "install": ["npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill general-equilibrium-model-builder"], "stars": 5013, "pushed": "2026-11-09T13:27:49Z", "license": "NOASSERTION", "plat": "julia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-anomalygen", "name": "cdf-anomalygen", "kind": "skill", "official": false, "desc": "Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-anomalygen", "install": ["npx skills add NVIDIA/skills --skill cdf-anomalygen"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-augmentation", "name": "cdf-augmentation", "kind": "skill", "official": false, "desc": "Use when authoring or validating Cosmos Data Factory (CDF) augmentation YAML configs, or running remote Cosmos Transfer (including Cosmos3 WSM controls), Cosmos Predict, image-edit, or image-to-video inference.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-augmentation", "install": ["npx skills add NVIDIA/skills --skill cdf-augmentation"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-auto-labeling", "name": "cdf-auto-labeling", "kind": "skill", "official": false, "desc": "Use when a user needs to get started with CDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, configure a stage, or pull the prebuilt stage containers from NGC (nvcr.io). Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-auto-labeling", "install": ["npx skills add NVIDIA/skills --skill cdf-auto-labeling"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-autonomous-vehicle-dataset-generation", "name": "cdf-autonomous-vehicle-dataset-generation", "kind": "skill", "official": false, "desc": "Prepare payloads, run and monitor the Autonomous Vehicle Dataset Generation Airflow workflow, and inspect results for driving-clip retrieval and Cosmos3-Nano variants. Existing clips with no retrieval use cdf-video-data-augmentation; seed-image event or anomaly generation uses cdf-event-video-generation.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-autonomous-vehicle-dataset-generation", "install": ["npx skills add NVIDIA/skills --skill cdf-autonomous-vehicle-dataset-generation"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-curation-and-retrieval", "name": "cdf-curation-and-retrieval", "kind": "skill", "official": false, "desc": "NVIDIA CDF curation and retrieval. Cosmos Curator video and image pipelines (split, dedup, shard, annotate) with filtering, captioning, and embedding. Cosmos Dataset Search ingest, search, and capability qualification. TAO data mining embeddings and neighbor matching. Ships domain event catalogs, including workplace employee conduct monitoring, which requires legal review before use. Use when a request names Cosmos Curator, Cosmos Dataset Search, CDF, or their Make and CLI surfaces. Not for mode", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-curation-and-retrieval", "install": ["npx skills add NVIDIA/skills --skill cdf-curation-and-retrieval"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-defect-image-generation", "name": "cdf-defect-image-generation", "kind": "skill", "official": false, "desc": "Generate synthetic PCB inspection images on Kubernetes with the SMT Defect Image Generation (DIG) DAG in CDF Orchestration. The DAG renders components in simulation, turns the renders into realistic golden images (sim2real), and uses AnomalyGen to add texture defects such as solder bridges, excess solder or missing components. Simulation can also render shift, tombstone and sideflip pose defects. Use it to build DIG payloads, pick a pipeline mode (full, simulation, sim2real or defect-only), chec", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-defect-image-generation", "install": ["npx skills add NVIDIA/skills --skill cdf-defect-image-generation"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/cdf-event-video-generation", "name": "cdf-event-video-generation", "kind": "skill", "official": false, "desc": "Run the CDF Orchestration Event Video Generation DAG on Kubernetes or OSMO - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Covers both event_vi", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/cdf-event-video-generation", "install": ["npx skills add NVIDIA/skills --skill cdf-event-video-generation"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-add-cmim-pretrain", "name": "kermt-add-cmim-pretrain", "kind": "skill", "official": false, "desc": "Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a one-time ckpt-conversion step prepended.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-add-cmim-pretrain", "install": ["npx skills add NVIDIA/skills --skill kermt-add-cmim-pretrain"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-continue-pretrain", "name": "kermt-continue-pretrain", "kind": "skill", "official": false, "desc": "Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, logs, and checkpoints to user-selected host directories.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-continue-pretrain", "install": ["npx skills add NVIDIA/skills --skill kermt-continue-pretrain"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "hugging face", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-embed", "name": "kermt-embed", "kind": "skill", "official": false, "desc": "Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-embed", "install": ["npx skills add NVIDIA/skills --skill kermt-embed"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-finetune", "name": "kermt-finetune", "kind": "skill", "official": false, "desc": "Finetune a pretrained KERMT encoder on a labeled CSV. Validate the checkpoint and data, prepare features, and run containerized training. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Write model bundles, prepared data, logs, and trained models to user-selected host directories.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-finetune", "install": ["npx skills add NVIDIA/skills --skill kermt-finetune"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-infer", "name": "kermt-infer", "kind": "skill", "official": false, "desc": "Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-infer", "install": ["npx skills add NVIDIA/skills --skill kermt-infer"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-monitor", "name": "kermt-monitor", "kind": "skill", "official": false, "desc": "Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-monitor", "install": ["npx skills add NVIDIA/skills --skill kermt-monitor"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-pretrain-scratch", "name": "kermt-pretrain-scratch", "kind": "skill", "official": false, "desc": "Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-pretrain-scratch", "install": ["npx skills add NVIDIA/skills --skill kermt-pretrain-scratch"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-kermt-setup", "name": "kermt-setup", "kind": "skill", "official": false, "desc": "Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-kermt-setup", "install": ["npx skills add NVIDIA/skills --skill kermt-setup"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "docker", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-molmim-nim", "name": "molmim-nim", "kind": "skill", "official": false, "desc": "Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-molmim-nim", "install": ["npx skills add NVIDIA/skills --skill molmim-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-msa-search-nim", "name": "msa-search-nim", "kind": "skill", "official": false, "desc": "Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docke", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-msa-search-nim", "install": ["npx skills add NVIDIA/skills --skill msa-search-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-msa-structure-prediction-pipeline", "name": "msa-structure-prediction-pipeline", "kind": "skill", "official": false, "desc": "NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with ma", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-msa-structure-prediction-pipeline", "install": ["npx skills add NVIDIA/skills --skill msa-structure-prediction-pipeline"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-nvmolkit-usage", "name": "nvmolkit-usage", "kind": "skill", "official": false, "desc": "Use when writing or debugging nvMolKit Python code for GPU-accelerated RDKit fingerprints, similarity, conformers, clustering, and molecular searches.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-nvmolkit-usage", "install": ["npx skills add NVIDIA/skills --skill nvmolkit-usage"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-openfold2-nim", "name": "openfold2-nim", "kind": "skill", "official": false, "desc": "Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Invoke whenever the user mentions OpenFold2, AlphaFold2-like monomer folding, protein sequence-to-structure prediction, A3M MSAs, mmCIF templates, hosted NVIDIA API calls, or local Docker deployment.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-openfold2-nim", "install": ["npx skills add NVIDIA/skills --skill openfold2-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-openfold3-nim", "name": "openfold3-nim", "kind": "skill", "official": false, "desc": "Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-openfold3-nim", "install": ["npx skills add NVIDIA/skills --skill openfold3-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-parabricks", "name": "parabricks", "kind": "skill", "official": false, "desc": "Plan NVIDIA Parabricks pbrun commands, translate CPU options and check runtime readiness. Use for individual tools, including planning-only questions.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-parabricks", "install": ["npx skills add NVIDIA/skills --skill parabricks"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-proteinmpnn-nim", "name": "proteinmpnn-nim", "kind": "skill", "official": false, "desc": "Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Sends user-provided PDB files and design parameters to NVIDIA's hosted API, authenticated with an environment API key, or to a user-selected local NIM. Use for sequence design, backbone redesign, fixed chains and residues, omit_AAs, sampling temperature, soluble model, local Docker, and multi-FASTA output.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-proteinmpnn-nim", "install": ["npx skills add NVIDIA/skills --skill proteinmpnn-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-rfd4-proteina-nim", "name": "rfd4-proteina-nim", "kind": "skill", "official": false, "desc": "Run RFD4-Proteina NIM for joint protein structure and sequence design, conditioned generation, and reward-guided search using the NVIDIA hosted API or a self-hosted NIM. Not for the older RFdiffusion contigs API.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-rfd4-proteina-nim", "install": ["npx skills add NVIDIA/skills --skill rfd4-proteina-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:NVIDIA/skills/skills/bionemo-rfdiffusion-nim", "name": "rfdiffusion-nim", "kind": "skill", "official": false, "desc": "Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backbone outputs for ProteinMPNN sequence design.", "repo": "https://github.com/NVIDIA/skills/tree/HEAD/skills/bionemo-rfdiffusion-nim", "install": ["npx skills add NVIDIA/skills --skill rfdiffusion-nim"], "stars": 3954, "pushed": "2026-11-12T14:09:43Z", "license": "Apache-2.0", "plat": "nvidia", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/adaptyv", "name": "adaptyv", "kind": "skill", "official": false, "desc": "Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein ", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/adaptyv", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill adaptyv"], "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/fluidsim", "name": "fluidsim", "kind": "skill", "official": false, "desc": "Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/fluidsim", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill fluidsim"], "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/pymc-bayesian-modeling", "name": "pymc-bayesian-modeling", "kind": "skill", "official": false, "desc": "Compatibility alias for the descriptive PyMC skill name. Delegate to the canonical local `pymc` payload while preserving route and README compatibility.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/pymc-bayesian-modeling", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill pymc-bayesian-modeling"], "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/statistical-analysis", "name": "statistical-analysis", "kind": "skill", "official": false, "desc": "Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/statistical-analysis", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill statistical-analysis"], "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/statistics-math", "name": "statistics-math", "kind": "skill", "official": false, "desc": "Statistics, probability, linear algebra, and mathematical foundations for data science", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/statistics-math", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill statistics-math"], "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/statsmodels", "name": "statsmodels", "kind": "skill", "official": false, "desc": "Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/statsmodels", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill statsmodels"], "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/tiledbvcf", "name": "tiledbvcf", "kind": "skill", "official": false, "desc": "Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/tiledbvcf", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill tiledbvcf"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "tiledb", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/torchdrug", "name": "torchdrug", "kind": "skill", "official": false, "desc": "Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/torchdrug", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill torchdrug"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "pytorch", "live": "Not checked (runs locally)"}, {"id": "skill:foryourhealth111-pixel/Vibe-Skills/bundled/skills/uniprot-database", "name": "uniprot-database", "kind": "skill", "official": false, "desc": "Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For broader biological evidence lookup across databases, use bio-database-evidence. Use this for direct HTTP/REST work or UniProt-specific control.", "repo": "https://github.com/foryourhealth111-pixel/Vibe-Skills/tree/HEAD/bundled/skills/uniprot-database", "install": ["npx skills add foryourhealth111-pixel/Vibe-Skills --skill uniprot-database"], "stars": 3816, "pushed": "2026-11-09T07:33:42Z", "license": "Apache-2.0", "plat": "uniprot", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/binder-design", "name": "binder-design", "kind": "skill", "official": false, "desc": "Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/binder-design", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill binder-design"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/binding-characterization", "name": "binding-characterization", "kind": "skill", "official": false, "desc": "Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/binding-characterization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill binding-characterization"], "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-differential", "name": "bio-hi-c-analysis-hic-differential", "kind": "skill", "official": false, "desc": "Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-hic-differential", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-hic-differential"], "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-visualization", "name": "bio-hi-c-analysis-hic-visualization", "kind": "skill", "official": false, "desc": "Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-hic-visualization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-hic-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-hi-c-analysis-loop-calling", "name": "bio-hi-c-analysis-loop-calling", "kind": "skill", "official": false, "desc": "Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-loop-calling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-loop-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-hi-c-analysis-matrix-operations", "name": "bio-hi-c-analysis-matrix-operations", "kind": "skill", "official": false, "desc": "Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-matrix-operations", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-matrix-operations"], "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-tad-detection", "name": "bio-hi-c-analysis-tad-detection", "kind": "skill", "official": false, "desc": "Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-hi-c-analysis-tad-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-hi-c-analysis-tad-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-imaging-mass-cytometry-cell-segmentation", "name": "bio-imaging-mass-cytometry-cell-segmentation", "kind": "skill", "official": false, "desc": "Cell segmentation from multiplexed tissue images. Covers deep learning (Cellpose, Mesmer) and classical approaches for nuclear and whole-cell segmentation. Use when extracting single-cell data from IMC or MIBI images after preprocessing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-cell-segmentation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-cell-segmentation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-imaging-mass-cytometry-data-preprocessing", "name": "bio-imaging-mass-cytometry-data-preprocessing", "kind": "skill", "official": false, "desc": "Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-data-preprocessing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-data-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-imaging-mass-cytometry-interactive-annotation", "name": "bio-imaging-mass-cytometry-interactive-annotation", "kind": "skill", "official": false, "desc": "Interactive cell type annotation for IMC data. Covers napari-based annotation, marker-guided labeling, training data generation, and annotation validation. Use when manually annotating cell types for training classifiers or validating automated phenotyping results.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-interactive-annotation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-interactive-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-imaging-mass-cytometry-phenotyping", "name": "bio-imaging-mass-cytometry-phenotyping", "kind": "skill", "official": false, "desc": "Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-phenotyping", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-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-imaging-mass-cytometry-quality-metrics", "name": "bio-imaging-mass-cytometry-quality-metrics", "kind": "skill", "official": false, "desc": "Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-quality-metrics", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-quality-metrics"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-imaging-mass-cytometry-spatial-analysis", "name": "bio-imaging-mass-cytometry-spatial-analysis", "kind": "skill", "official": false, "desc": "Spatial analysis of cell neighborhoods and interactions in IMC data. Covers neighbor graphs, spatial statistics, and interaction testing. Use when analyzing spatial relationships between cell types, testing for neighborhood enrichment, or identifying cell-cell interaction patterns in imaging mass cytometry data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-imaging-mass-cytometry-spatial-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-spatial-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-immunoinformatics-epitope-prediction", "name": "bio-immunoinformatics-epitope-prediction", "kind": "skill", "official": false, "desc": "Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic peptides.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-immunoinformatics-epitope-prediction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-epitope-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-immunoinformatics-immunogenicity-scoring", "name": "bio-immunoinformatics-immunogenicity-scoring", "kind": "skill", "official": false, "desc": "Score and prioritize neoantigens and epitopes for immunogenicity using multi-factor models combining MHC binding, processing, expression, and sequence features. Rank candidates for vaccine design. Use when prioritizing epitopes for vaccine development or identifying the most immunogenic neoantigens.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-immunoinformatics-immunogenicity-scoring", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-immunogenicity-scoring"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-immunoinformatics-mhc-binding-prediction", "name": "bio-immunoinformatics-mhc-binding-prediction", "kind": "skill", "official": false, "desc": "Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-immunoinformatics-mhc-binding-prediction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-mhc-binding-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-immunoinformatics-neoantigen-prediction", "name": "bio-immunoinformatics-neoantigen-prediction", "kind": "skill", "official": false, "desc": "Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response biomarkers from tumor sequencing data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-immunoinformatics-neoantigen-prediction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-neoantigen-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-immunoinformatics-tcr-epitope-binding", "name": "bio-immunoinformatics-tcr-epitope-binding", "kind": "skill", "official": false, "desc": "Predict TCR-epitope specificity using ERGO-II and deep learning models for T-cell receptor antigen recognition. Match TCRs to their cognate epitopes or predict TCR targets. Use when analyzing TCR repertoire specificity or identifying antigen-reactive T-cells.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-immunoinformatics-tcr-epitope-binding", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-immunoinformatics-tcr-epitope-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-isoform-switching", "name": "bio-isoform-switching", "kind": "skill", "official": false, "desc": "Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-isoform-switching", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-isoform-switching"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-long-read-sequencing-clair3-variants", "name": "bio-long-read-sequencing-clair3-variants", "kind": "skill", "official": false, "desc": "Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-long-read-sequencing-clair3-variants", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-long-read-sequencing-clair3-variants"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-long-read-sequencing-isoseq-analysis", "name": "bio-long-read-sequencing-isoseq-analysis", "kind": "skill", "official": false, "desc": "Analyze PacBio Iso-Seq data for full-length isoform discovery and quantification. Use when characterizing transcript diversity or identifying novel splice variants.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-long-read-sequencing-isoseq-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-long-read-sequencing-isoseq-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-long-read-sequencing-nanopore-methylation", "name": "bio-long-read-sequencing-nanopore-methylation", "kind": "skill", "official": false, "desc": "Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-long-read-sequencing-nanopore-methylation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-long-read-sequencing-nanopore-methylation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-longitudinal-monitoring", "name": "bio-longitudinal-monitoring", "kind": "skill", "official": false, "desc": "Tracks ctDNA dynamics over time for treatment response monitoring using serial liquid biopsy samples. Analyzes tumor fraction trends, mutation clearance kinetics, and defines molecular response criteria. Use when monitoring patients during therapy or detecting molecular relapse before clinical progression.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-longitudinal-monitoring", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longitudinal-monitoring"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-longread-alignment", "name": "bio-longread-alignment", "kind": "skill", "official": false, "desc": "Align long reads using minimap2 for Oxford Nanopore and PacBio data. Supports various presets for different read types and applications. Use when aligning ONT or PacBio reads to a reference genome for variant calling, SV detection, or coverage analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-longread-alignment", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-alignment"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-longread-medaka", "name": "bio-longread-medaka", "kind": "skill", "official": false, "desc": "Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-longread-medaka", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-medaka"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-longread-qc", "name": "bio-longread-qc", "kind": "skill", "official": false, "desc": "Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-longread-qc", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-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-longread-structural-variants", "name": "bio-longread-structural-variants", "kind": "skill", "official": false, "desc": "Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-longread-structural-variants", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-longread-structural-variants"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "sniffles", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-lipidomics", "name": "bio-metabolomics-lipidomics", "kind": "skill", "official": false, "desc": "Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-lipidomics", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-lipidomics"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "lipidsearch", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-metabolite-annotation", "name": "bio-metabolomics-metabolite-annotation", "kind": "skill", "official": false, "desc": "Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-metabolite-annotation", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-metabolite-annotation"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "metaboanalyst", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-msdial-preprocessing", "name": "bio-metabolomics-msdial-preprocessing", "kind": "skill", "official": false, "desc": "MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-msdial-preprocessing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-msdial-preprocessing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "ms-dial", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-normalization-qc", "name": "bio-metabolomics-normalization-qc", "kind": "skill", "official": false, "desc": "Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-normalization-qc", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-normalization-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-metabolomics-pathway-mapping", "name": "bio-metabolomics-pathway-mapping", "kind": "skill", "official": false, "desc": "Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when interpreting metabolomics results in the context of biochemical pathways.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-pathway-mapping", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-pathway-mapping"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "kegg", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-statistical-analysis", "name": "bio-metabolomics-statistical-analysis", "kind": "skill", "official": false, "desc": "Statistical analysis for metabolomics data. Covers univariate testing, multivariate methods (PCA, PLS-DA), and biomarker discovery. Use when identifying differentially abundant metabolites or building classification models.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-statistical-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-statistical-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "metaboanalyst", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metabolomics-targeted-analysis", "name": "bio-metabolomics-targeted-analysis", "kind": "skill", "official": false, "desc": "Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-targeted-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-targeted-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-metabolomics-xcms-preprocessing", "name": "bio-metabolomics-xcms-preprocessing", "kind": "skill", "official": false, "desc": "XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metabolomics-xcms-preprocessing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metabolomics-xcms-preprocessing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "xcms", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-abundance", "name": "bio-metagenomics-abundance", "kind": "skill", "official": false, "desc": "Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification output.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-abundance", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-abundance"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "kraken2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-amr-detection", "name": "bio-metagenomics-amr-detection", "kind": "skill", "official": false, "desc": "Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-amr-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-amr-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "amrfinderplus", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-functional-profiling", "name": "bio-metagenomics-functional-profiling", "kind": "skill", "official": false, "desc": "Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-functional-profiling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-functional-profiling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "humann3", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-kraken", "name": "bio-metagenomics-kraken", "kind": "skill", "official": false, "desc": "Taxonomic classification of metagenomic reads using Kraken2. Fast k-mer based classification against RefSeq database. Use when performing initial taxonomic classification of shotgun metagenomic reads before abundance estimation with Bracken.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-kraken", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-kraken"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "kraken2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-metaphlan", "name": "bio-metagenomics-metaphlan", "kind": "skill", "official": false, "desc": "Marker gene-based taxonomic profiling using MetaPhlAn 4. Provides accurate species-level relative abundances using clade-specific markers. Use when accurate taxonomic profiling is needed and computational resources are limited, or for comparison with HMP/other MetaPhlAn studies.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-metaphlan", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-metaphlan"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "metaphlan", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-strain-tracking", "name": "bio-metagenomics-strain-tracking", "kind": "skill", "official": false, "desc": "Track bacterial strains using MASH, sourmash, fastANI, and inStrain. Compare genomes, detect contamination, and monitor strain-level variation. Use when needing sub-species resolution for outbreak tracking, transmission analysis, or within-host strain dynamics.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-strain-tracking", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-strain-tracking"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "mash", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-metagenomics-visualization", "name": "bio-metagenomics-visualization", "kind": "skill", "official": false, "desc": "Visualize metagenomic profiles using R (phyloseq, microbiome) and Python (matplotlib, seaborn). Create stacked bar plots, heatmaps, PCA plots, and diversity analyses. Use when creating publication-quality figures from MetaPhlAn, Bracken, or other taxonomic profiling output.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-metagenomics-visualization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-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-methylation-based-detection", "name": "bio-methylation-based-detection", "kind": "skill", "official": false, "desc": "Analyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel. Identifies cancer-specific methylation signatures and performs tissue-of-origin deconvolution. Use when using methylation biomarkers for early cancer detection or minimal residual disease.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-methylation-based-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-based-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "cfmedip-seq", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-methylation-bismark-alignment", "name": "bio-methylation-bismark-alignment", "kind": "skill", "official": false, "desc": "Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-methylation-bismark-alignment", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-bismark-alignment"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "bismark", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-methylation-calling", "name": "bio-methylation-calling", "kind": "skill", "official": false, "desc": "Extract methylation calls from Bismark BAM files using bismark_methylation_extractor. Generates per-cytosine reports for CpG, CHG, and CHH contexts. Use when extracting methylation levels from aligned bisulfite sequencing data for downstream analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-methylation-calling", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-calling"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "bismark", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-methylation-dmr-detection", "name": "bio-methylation-dmr-detection", "kind": "skill", "official": false, "desc": "Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-methylation-dmr-detection", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-dmr-detection"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "methylkit", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-methylation-methylkit", "name": "bio-methylation-methylkit", "kind": "skill", "official": false, "desc": "DNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-methylation-methylkit", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-methylkit"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "methylkit", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-amplicon-processing", "name": "bio-microbiome-amplicon-processing", "kind": "skill", "official": false, "desc": "Amplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-amplicon-processing", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-amplicon-processing"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "dada2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-differential-abundance", "name": "bio-microbiome-differential-abundance", "kind": "skill", "official": false, "desc": "Differential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-differential-abundance", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-differential-abundance"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "aldex2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-diversity-analysis", "name": "bio-microbiome-diversity-analysis", "kind": "skill", "official": false, "desc": "Alpha and beta diversity analysis for microbiome data. Calculate within-sample richness, evenness, and between-sample dissimilarity with phyloseq and vegan. Use when comparing community composition across samples or testing for group differences in microbiome structure.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-diversity-analysis", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-diversity-analysis"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "phyloseq", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-functional-prediction", "name": "bio-microbiome-functional-prediction", "kind": "skill", "official": false, "desc": "Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-functional-prediction", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-functional-prediction"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "picrust2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-qiime2-workflow", "name": "bio-microbiome-qiime2-workflow", "kind": "skill", "official": false, "desc": "QIIME2 command-line workflow for 16S/ITS amplicon analysis. Alternative to DADA2/phyloseq R workflow with built-in provenance tracking. Use when preferring CLI over R, needing reproducible provenance, or working within QIIME2 ecosystem.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-qiime2-workflow", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-qiime2-workflow"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "qiime2", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-microbiome-taxonomy-assignment", "name": "bio-microbiome-taxonomy-assignment", "kind": "skill", "official": false, "desc": "Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-microbiome-taxonomy-assignment", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-microbiome-taxonomy-assignment"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "silva", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-molecular-descriptors", "name": "bio-molecular-descriptors", "kind": "skill", "official": false, "desc": "Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-molecular-descriptors", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-molecular-descriptors"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "rdkit", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-molecular-io", "name": "bio-molecular-io", "kind": "skill", "official": false, "desc": "Reads, writes, and converts molecular file formats (SMILES, SDF, MOL2, PDB) using RDKit and Open Babel. Handles structure parsing, canonicalization, and full standardization pipeline including sanitization, normalization, and tautomer canonicalization. Use when loading chemical libraries, converting formats, or preparing molecules for analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-molecular-io", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-molecular-io"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "open babel", "live": "Not checked (runs locally)"}, {"id": "skill:FreedomIntelligence/OpenClaw-Medical-Skills/skills/bio-multi-omics-data-harmonization", "name": "bio-multi-omics-data-harmonization", "kind": "skill", "official": false, "desc": "Preprocessing and harmonization of multi-omics data before integration. Covers normalization, batch correction, feature alignment, and missing value handling across data types. Use when preparing multi-omics datasets for integration analysis.", "repo": "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/HEAD/skills/bio-multi-omics-data-harmonization", "install": ["npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-multi-omics-data-harmonization"], "stars": 3107, "pushed": "2026-07-21T01:01:51Z", "license": null, "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.usamahz/cpu-perf", "name": "CPU Performance Engineering", "kind": "mcp", "official": null, "desc": "A CPU performance engineering brain: a vetted reading list, benchmarks and cited source passages", "repo": "https://github.com/usamahz/cpu-performance-engineering", "install": ["pypi:cpu-perf"], "stars": 1159, "pushed": "2026-10-08T18:41:07Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.Kentucky-ai/opentakeoff", "name": "OpenTakeoff", "kind": "mcp", "official": null, "desc": "Construction takeoff for AI agents: load plans, set scale, measure, count, export with provenance.", "repo": "https://github.com/Kentucky-ai/opentakeoff", "install": ["npm:opentakeoff-mcp"], "stars": 803, "pushed": "2026-11-12T19:57:57Z", "license": "AGPL-3.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "skill:amd/skills/skills/flydsl-kernel-authoring", "name": "flydsl-kernel-authoring", "kind": "skill", "official": false, "desc": "Comprehensive reference for authoring FlyDSL GPU kernels on AMD GPUs. Covers the layout algebra, tiled copy/MMA, buffer ops, loop-carried range loops, SharedAllocator (LDS), autotuning, and common patterns. Use when writing, reviewing, or understanding FlyDSL kernel code.", "repo": "https://github.com/amd/skills/tree/HEAD/skills/flydsl-kernel-authoring", "install": ["npx skills add amd/skills --skill flydsl-kernel-authoring"], "stars": 490, "pushed": "2026-11-13T11:45:41Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.gokeshenzhen/traceweave", "name": "TraceWeave", "kind": "mcp", "official": null, "desc": "Portable MCP runtime for RTL logs and VCD; repository setup adds FSDB and site EDA integration", "repo": "https://github.com/gokeshenzhen/TraceWeave", "install": ["pypi:traceweave-mcp"], "stars": 131, "pushed": "2026-10-13T11:54:53Z", "license": "MIT", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.yxy050208/multisim-mcp", "name": "Multisim MCP", "kind": "mcp", "official": null, "desc": "Generate Multisim circuits, run simulations, export data, and create experiment reports.", "repo": "https://github.com/yxy050208/multisim-mcp", "install": ["pypi:multisim-mcp"], "stars": 39, "pushed": "2026-11-02T06:42:41Z", "license": "MIT", "plat": "multisim", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.luarss/openroad-mcp", "name": "io.github.luarss/openroad-mcp", "kind": "mcp", "official": null, "desc": "The OpenROAD MCP server - interactive EDA sessions via Model Context Protocol", "repo": "https://github.com/luarss/openroad-mcp", "install": ["pypi:openroad-mcp", "oci:ghcr.io/luarss/openroad-mcp:0.5.3"], "stars": 35, "pushed": "2026-11-13T00:29:54Z", "license": "BSD-3-Clause", "plat": "openroad", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-brainflow", "name": "BrainFlow Biosensing Boards", "kind": "mcp", "official": null, "desc": "MCP server for EEG/EMG/ECG/PPG boards via BrainFlow (OpenBCI, Muse, Neurosity, Unicorn, BrainBit).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-brainflow"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "brainflow", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-epics", "name": "EPICS Channel Access", "kind": "mcp", "official": null, "desc": "MCP server for EPICS Channel Access: read, monitor and guarded writes of process variables.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-epics"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "epics", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-scpi", "name": "Generic SCPI Instrument", "kind": "mcp", "official": null, "desc": "Generic MCP server for any SCPI instrument over VISA, TCP or serial, with command allow/deny lists.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-scpi"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-ika", "name": "IKA Hotplate & Overhead Stirrer", "kind": "mcp", "official": null, "desc": "MCP server for IKA hotplate stirrers and overhead stirrers (NAMUR commands).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-ika"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "ika", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-julabo", "name": "JULABO Circulator", "kind": "mcp", "official": null, "desc": "MCP server for JULABO heating and refrigerated circulators (CORIO, MAGIO, DYNEO).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-julabo"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "julabo", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-keithley-smu", "name": "Keithley SourceMeter SMU", "kind": "mcp", "official": null, "desc": "MCP server for Keithley SourceMeter SMUs (2400 series SCPI, 2450 family SCPI, 2600B TSP).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-keithley-smu"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "keithley", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-labjack", "name": "LabJack T-series DAQ", "kind": "mcp", "official": null, "desc": "MCP server for LabJack T4, T7/T7-Pro and T8 DAQ devices (LJM).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-labjack"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "labjack", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-lakeshore", "name": "Lake Shore Temperature Controller", "kind": "mcp", "official": null, "desc": "MCP server for Lake Shore Cryotronics 335/336/350 temperature controllers.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-lakeshore"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "lake shore", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-ms-worklist", "name": "LC-MS Worklist Builder", "kind": "mcp", "official": null, "desc": "MCP server that builds and validates LC-MS worklists for MassLynx, SCIEX OS, MassHunter, Xcalibur.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-ms-worklist"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-ms-data", "name": "Mass Spectrometry Data (mzML, Bruker TDF, vendor conversion)", "kind": "mcp", "official": null, "desc": "MCP server for LC-MS data in mzML/mzMLb and Bruker timsTOF files; vendor formats via msconvert.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-ms-data"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "bruker", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-mettler-toledo", "name": "Mettler Toledo Balance", "kind": "mcp", "official": null, "desc": "MCP server for Mettler Toledo laboratory balances (MT-SICS): weigh, tare, zero, log drift, adjust.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-mettler-toledo"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "mettler toledo", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-micro-manager", "name": "Micro-Manager Microscope", "kind": "mcp", "official": null, "desc": "MCP server for microscopes controlled by Micro-Manager (pymmcore-plus).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-micro-manager"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "micro-manager", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-new-era", "name": "New Era Syringe Pump", "kind": "mcp", "official": null, "desc": "MCP server for New Era Pump Systems syringe pumps (NE-1000 family, RS-232).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-new-era"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "new era pump systems", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-ni-daqmx", "name": "NI-DAQmx DAQ Devices", "kind": "mcp", "official": null, "desc": "MCP server for National Instruments DAQ devices (NI-DAQmx).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-ni-daqmx"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "national instruments", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-ocean-spectrometer", "name": "Ocean Insight Spectrometer", "kind": "mcp", "official": null, "desc": "MCP server for Ocean Insight / Ocean Optics spectrometers (python-seabreeze).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-ocean-spectrometer"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "ocean insight", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-opentrons", "name": "Opentrons OT-2 / Flex", "kind": "mcp", "official": null, "desc": "MCP server for Opentrons OT-2 and Flex liquid-handling robots (HTTP API).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-opentrons"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "opentrons", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-palmsens", "name": "PalmSens Potentiostat", "kind": "mcp", "official": null, "desc": "MCP server for PalmSens potentiostats running MethodSCRIPT (EmStat Pico, EmStat4, Sensit, Nexus).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-palmsens"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "palmsens", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-pfeiffer-tpg", "name": "Pfeiffer Vacuum Gauge Controller", "kind": "mcp", "official": null, "desc": "MCP server for Pfeiffer Vacuum TPG 361/362/366 and TPG 261/262 gauge controllers.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-pfeiffer-tpg"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "pfeiffer vacuum", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-rigol-scope", "name": "Rigol Oscilloscope", "kind": "mcp", "official": null, "desc": "MCP server for Rigol oscilloscopes (DS1000Z, MSO5000, DHO series): measurements and waveforms.", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-rigol-scope"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "rigol", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-sartorius", "name": "Sartorius Balance", "kind": "mcp", "official": null, "desc": "MCP server for Sartorius laboratory balances (SBI protocol).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": ["pypi:labmcp-sartorius"], "stars": 30, "pushed": "2026-09-26T22:47:14Z", "license": "Apache-2.0", "plat": "sartorius", "live": "Not checked (runs locally)"}, {"id": "mcp:io.github.K-Dense-AI/labmcp-sila2", "name": "SiLA 2 Bridge", "kind": "mcp", "official": null, "desc": "MCP bridge to any SiLA 2 server (lab-automation standard).", "repo": "https://github.com/K-Dense-AI/lab-instrument-mcps", "install": 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