FreedomIntelligence/OpenClaw-Medical-Skills: 896 Agent Skills
The largest open-source medical AI skills library for OpenClaw🦞.
| Repository | FreedomIntelligence/OpenClaw-Medical-Skills |
|---|---|
| GitHub stars | 3,053 |
| Skills | 896 (the first 80 are listed below) |
| Category | Data and analytics |
| Last updated | Jul 21, 2026 |
| Install counts | FreedomIntelligence/OpenClaw-Medical-Skills on skills.sh, Vercel's skills directory, which shows installs and security audits per skill |
Install FreedomIntelligence/OpenClaw-Medical-Skills
| All skills, any agent | npx skills add FreedomIntelligence/OpenClaw-Medical-Skills |
|---|---|
| One skill | npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill aav-vector-design-agent |
| Only for Claude Code | npx skills add FreedomIntelligence/OpenClaw-Medical-Skills -a claude-code, or copy a skill folder to ~/.claude/skills/ (all projects) or .claude/skills/ (one project) |
npx skills is the open-source skills CLI; it asks which agents to install for. Skills can include scripts that your agent will run: read a skill before installing it, as you would any code.
Skills in FreedomIntelligence/OpenClaw-Medical-Skills
| Skill | What it does |
|---|---|
| aav-vector-design-agent | — |
| adaptyv | 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… |
| adhd-daily-planner | Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds. |
| aeon | 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… |
| agent-browser | Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful, not just when the user explicitly asks. |
| agentd-drug-discovery | — |
| ai-analyzer | AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。 |
| alphafold-database | Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology. |
| alphafold | Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain… |
| anndata | This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq… |
| MAGE | — |
| antibody-design-agent | — |
| arboreto | Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed… |
| armored-cart-design-agent | — |
| arxiv-search | Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search. |
| autonomous-oncology-agent | — |
| bayesian-optimizer | — |
| benchling-integration | Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation. |
| bgpt-paper-search | Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding… |
| bindcraft | End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization,… |
| binder-design | 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… |
| binding-characterization | 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. |
| bindingdb-database | Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies. |
| bio-admet-prediction | Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing… |
| bio-alignment-files-bam-statistics | — |
| bio-alignment-filtering | — |
| bio-alignment-indexing | — |
| bio-alignment-io | Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats. |
| bio-alignment-msa-parsing | Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments. |
| bio-alignment-msa-statistics | Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns. |
| bio-alignment-pairwise | Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences. |
| bio-alignment-sorting | — |
| bio-alignment-validation | — |
| bio-atac-seq-atac-peak-calling | Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling. |
| bio-atac-seq-atac-qc | Quality control metrics for ATAC-seq data including fragment size distribution, TSS enrichment, FRiP, and library complexity. Use when assessing ATAC-seq library quality before or after peak calling to identify problematic samples. |
| bio-atac-seq-differential-accessibility | Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments. |
| bio-atac-seq-footprinting | Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting. |
| bio-atac-seq-motif-deviation | Analyze transcription factor motif accessibility variability using chromVAR. Use when identifying which TF motifs show variable accessibility across samples or conditions in ATAC-seq data. |
| bio-atac-seq-nucleosome-positioning | Extract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis. Use when analyzing chromatin organization, identifying nucleosome-free regions at promoters, or characterizing nucleosome occupancy patterns from ATAC-seq fragment size distributions. |
| bio-basecalling | Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Guppy is deprecated; use Dorado for all new analyses. |
| bio-batch-downloads | — |
| bio-batch-processing | Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories. |
| bio-bedgraph-handling | — |
| bio-blast-searches | — |
| bio-causal-genomics-colocalization-analysis | Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant. |
| bio-causal-genomics-fine-mapping | Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to… |
| bio-causal-genomics-mediation-analysis | Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates… |
| bio-causal-genomics-mendelian-randomization | Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects… |
| bio-causal-genomics-pleiotropy-detection | Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instruments, or running… |
| bio-cfdna-preprocessing | Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream… |
| bio-chipseq-differential-binding | Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions. |
| bio-chipseq-motif-analysis | De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences. |
| bio-chipseq-peak-annotation | Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features. |
| bio-chipseq-peak-calling | ChIP-seq peak calling using MACS3 (or MACS2). Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports input control, fragment size modeling, and various output formats including narrowPeak and broadPeak BED files. Use when calling peaks from ChIP-seq… |
| bio-chipseq-qc | ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq… |
| bio-chipseq-super-enhancers | Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains. |
| bio-chipseq-visualization | Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks. |
| bio-clinical-databases-clinvar-lookup | Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes. |
| bio-clinical-databases-dbsnp-queries | Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information. |
| bio-clinical-databases-gnomad-frequencies | Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population. |
| bio-clinical-databases-hla-typing | Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening. |
| bio-clinical-databases-myvariant-queries | Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously. |
| bio-clinical-databases-pharmacogenomics | Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics. |
| bio-clinical-databases-polygenic-risk | Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants. |
| bio-clinical-databases-somatic-signatures | Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes. |
| bio-clinical-databases-tumor-mutational-burden | Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity. |
| bio-clinical-databases-variant-prioritization | Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing. |
| bio-clip-seq-binding-site-annotation | — |
| bio-clip-seq-clip-alignment | — |
| bio-clip-seq-clip-motif-analysis | — |
| bio-clip-seq-clip-peak-calling | — |
| bio-clip-seq-clip-preprocessing | — |
| bio-codon-usage | — |
| bio-comparative-genomics-ancestral-reconstruction | — |
| bio-comparative-genomics-hgt-detection | — |
| bio-comparative-genomics-ortholog-inference | — |
| bio-comparative-genomics-positive-selection | — |
| bio-comparative-genomics-synteny-analysis | — |
| bio-compressed-files | Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files. |
| bio-consensus-sequences | Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes. |
Names and descriptions come from each skill's SKILL.md and are written by the pack's authors.
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