[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-74130":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":34,"readmeContent":35,"aiSummary":36,"trendingCount":16,"starSnapshotCount":16,"syncStatus":37,"lastSyncTime":38,"discoverSource":39},74130,"OpenClaw-Medical-Skills","FreedomIntelligence\u002FOpenClaw-Medical-Skills","FreedomIntelligence","The largest open-source medical AI skills library for OpenClaw🦞.","",null,"Python",2626,364,19,10,0,15,47,163,45,106.69,false,"main",true,[26,27,28,29,30,31,32,33],"awesome","claude-code","clawhub","medical","nanoclaw","openclaw","openclaw-skills","skills","2026-06-12 04:01:13","# OpenClaw Medical Skills\n\n\u003Cdiv align=\"center\">\n\n[![GitHub Stars](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills?style=for-the-badge&logo=github&color=gold)](https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills\u002Fstargazers)\n[![GitHub Forks](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fforks\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills?style=for-the-badge&logo=github&color=blue)](https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills\u002Fnetwork\u002Fmembers)\n[![GitHub Issues](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fissues\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills?style=for-the-badge&logo=github)](https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills\u002Fissues)\n[![Skills Count](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSkills-869-brightgreen?style=for-the-badge&logo=data:image\u002Fsvg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCI+PHBhdGggZmlsbD0id2hpdGUiIGQ9Ik0xMiAyQzYuNDggMiAyIDYuNDggMiAxMnM0LjQ4IDEwIDEwIDEwIDEwLTQuNDggMTAtMTBTMTcuNTIgMiAxMiAyem0tMiAxNWwtNS01IDEuNDEtMS40MUwxMCAxNC4xN2w3LjU5LTcuNTlMMTkgOGwtOSA5eiIvPjwvc3ZnPg==)](https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills\u002Ftree\u002Fmain\u002Fskills)\n[![License](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-purple?style=for-the-badge)](LICENSE)\n[![Platform](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPlatform-OpenClaw%20%7C%20NanoClaw-orange?style=for-the-badge)](https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fopenclaw)\n\n**The largest open-source medical AI skill library for OpenClaw.**\n\n*869 curated skills · Clinical · Genomics · Drug Discovery · Bioinformatics · Medical Devices*\n\n[English](#) | [中文](README_zh.md)\n\n\u003C\u002Fdiv>\n\n---\n\n## What Is This?\n\n**OpenClaw Medical Skills** is a curated collection of **869 AI agent skills** covering the full spectrum of biomedical and clinical research. These skills are designed for [OpenClaw](https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fopenclaw) \u002F [NanoClaw](https:\u002F\u002Fgithub.com\u002Fqwibitai\u002Fnanoclaw) — Claude-based personal AI assistant frameworks — and transform a general-purpose AI agent into a powerful medical and scientific research companion.\n\nEach skill is a self-contained module (a `SKILL.md` file) that:\n- Teaches the agent specialized domain knowledge and workflows\n- Connects to real databases, APIs, and computational tools\n- Produces structured, clinically or scientifically relevant outputs\n\n> We benefit from the open-source community. The full collection of resources can be found here: [Awesome LLM Resources](https:\u002F\u002Fgithub.com\u002FWangRongsheng\u002Fawesome-LLM-resources?tab=readme-ov-file#%E6%8A%80%E8%83%BD-Skills)\n\n### Why This Collection Matters\n\n| Without Skills | With OpenClaw Medical Skills |\n|---|---|\n| Generic AI responses about medicine | Real PubMed \u002F ClinicalTrials.gov \u002F FDA queries |\n| No bioinformatics capability | RNA-seq, scRNA-seq, GWAS, variant calling pipelines |\n| No drug intelligence | ChEMBL, DrugBank, DDI prediction, pharmacovigilance |\n| No clinical documentation | SOAP notes, discharge summaries, prior auth decisions |\n| No genomics support | VCF annotation, ACMG classification, PRS calculation |\n| No regulatory guidance | FDA, CE mark, IEC 62304, ISO 14971 compliance |\n\nThis collection aggregates skills from **12+ open-source skill repositories** spanning academic research tools, clinical workflows, regulatory frameworks, and cutting-edge AI-driven protein design — giving your AI agent capabilities comparable to a team of specialized research scientists.\n\n---\n\n## Installation\n\n### Requirements\n\n- [OpenClaw](https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fopenclaw) installed and running, **or** [NanoClaw](https:\u002F\u002Fgithub.com\u002Fqwibitai\u002Fnanoclaw) as an alternative\n- Git (for cloning this repo)\n\n---\n\n### For OpenClaw Users\n\nOpenClaw loads skills from two locations:\n\n| Priority | Path | Scope |\n|---|---|---|\n| High | `\u003Cworkspace>\u002Fskills\u002F` | Per-workspace (recommended) |\n| Low | `~\u002F.openclaw\u002Fskills\u002F` | Global, shared across all agents |\n\n#### Method 1 — Clone and Copy (Recommended)\n\n```bash\n# Clone this repository (skills only — skips large data files)\ngit clone --depth=1 --no-checkout https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills.git\ncd OpenClaw-Medical-Skills\ngit sparse-checkout init --cone\ngit sparse-checkout set skills\ngit checkout main\n\n# Install to your workspace skills directory\ncp -r skills\u002F* \u003Cyour-workspace>\u002Fskills\u002F\n\n# Or install globally (available to all agents)\ncp -r skills\u002F* ~\u002F.openclaw\u002Fskills\u002F\n```\n\n> **Note:** Some skills bundle large data files (databases, datasets). The\n> sparse-checkout method above avoids downloading them. If you need the full\n> repo including all data, install [Git LFS](https:\u002F\u002Fgit-lfs.com) first, then\n> run `git clone https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills.git`.\n\nSkills are picked up automatically on the next session. No restart needed.\n\n#### Method 2 — OpenClaw CLI\n\nIf you use the [OpenClaw plugin registry](https:\u002F\u002Fclawhub.com), you can search and install individual skills from there. For bulk install from this collection, Method 1 is faster.\n\n```bash\nopenclaw plugins install \u003Cskill-slug>    # install a single skill\nopenclaw plugins update                  # update all installed skills\n```\n\n#### Method 3 — Configure Extra Directories\n\nTo point OpenClaw at a cloned copy of this repo permanently, add it to `~\u002F.openclaw\u002Fopenclaw.json`:\n\n```json\n{\n  \"plugins\": {\n    \"local\": [\"\u002Fpath\u002Fto\u002FOpenClaw-Medical-Skills\"]\n  }\n}\n```\n\nThis mounts the entire collection without copying files.\n\n#### Method 4 — Install Selected Skills Only\n\nPick skills relevant to your domain:\n\n```bash\n# Example: clinical + drug discovery stack\nSKILLS=(\n  \"clinical-reports\"\n  \"tooluniverse-drug-research\"\n  \"tooluniverse-pharmacovigilance\"\n  \"clinicaltrials-database\"\n  \"biomedical-search\"\n  \"tooluniverse-drug-drug-interaction\"\n)\n\nfor skill in \"${SKILLS[@]}\"; do\n  cp -r OpenClaw-Medical-Skills\u002Fskills\u002F$skill ~\u002F.openclaw\u002Fskills\u002F\ndone\n```\n\n---\n\n### For NanoClaw Users\n\nNanoClaw loads skills into agent containers at startup from `container\u002Fskills\u002F`.\n\n```bash\n# Clone and copy into NanoClaw container skills directory\ngit clone https:\u002F\u002Fgithub.com\u002FFreedomIntelligence\u002FOpenClaw-Medical-Skills.git\ncp -r OpenClaw-Medical-Skills\u002Fskills\u002F* \u002Fpath\u002Fto\u002Fnanoclaw\u002Fcontainer\u002Fskills\u002F\n\n# Rebuild the container to apply\ncd \u002Fpath\u002Fto\u002Fnanoclaw\n.\u002Fcontainer\u002Fbuild.sh\n```\n\n---\n\n### Verification\n\nAfter installation, ask your agent:\n\n```\nWhat medical and clinical skills do you have available?\n```\n\nYour agent should list the installed skills with their capabilities.\n\n---\n\n## Skills Overview\n\n| Category | Count | Highlights |\n|---|---|---|\n| General & Core | 10 | Browser\u002Fsearch, document tools, and developer workflow utilities |\n| Medical & Clinical | 119 | Clinical reports, CDS, oncology, imaging, and healthcare AI |\n| Scientific Databases | 43 | Genomics\u002Fprotein\u002Fdrug databases and biomedical knowledge retrieval |\n| Bioinformatics (gptomics) | 239 | Variant analysis, sequencing QC, DE, pathways, single-cell, and epigenomics |\n| Omics & Computational Biology | 59 | Single-cell\u002Fspatial, proteomics, cheminformatics, and protein design tools |\n| ClawBio Pipelines | 21 | Orchestration pipelines for scRNA, GWAS, ancestry, and structural workflows |\n| BioOS Extended Suite | 285 | Extended agent suite for oncology, immunology, clinical AI, and infrastructure |\n| Data Science & Tools | 93 | Statistics, visualization, automation, simulation, and scientific tooling |\n| **Total** | **869** | |\n\n---\n\n## Table of Contents\n\n### General & Core\n- [General Tools](#general-tools)\n\n### Medical & Clinical\n- [Medical Tools](#medical-tools)\n- [Drug Safety & Chemical Biology](#drug-safety--chemical-biology)\n- [Medical Imaging & Pathology](#medical-imaging--pathology)\n- [Healthcare ML & Clinical AI](#healthcare-ml--clinical-ai)\n- [Mental Health & Crisis Intervention](#mental-health--crisis-intervention)\n- [Health & Wellness Analytics](#health--wellness-analytics)\n- [Medical Device & Regulatory](#medical-device--regulatory)\n- [Medical Device Software (meddev-agent-skills)](#medical-device--regulatory)\n\n### Scientific Databases\n- [Scientific Databases (Genomics & Variants)](#scientific-databases-genomics--variants)\n- [Scientific Databases (Proteins, Pathways & Drugs)](#scientific-databases-proteins-pathways--drugs)\n- [Cancer Genomics Databases](#cancer-genomics-databases)\n- [Genomic & Molecular Databases](#genomic--molecular-databases)\n- [Structural Biology & Drug Discovery](#structural-biology--drug-discovery)\n\n### Bioinformatics (gptomics bio-* suite)\n- [Bioinformatics Tools & Pipelines](#bioinformatics-tools--pipelines)\n- [Bioinformatics — Clinical Databases & Variant Analysis](#bioinformatics--clinical-databases--variant-analysis)\n- [Bioinformatics — Sequencing & Read QC](#bioinformatics--sequencing--read-qc)\n- [Bioinformatics — Differential Expression & Transcriptomics](#bioinformatics--differential-expression--transcriptomics)\n- [Bioinformatics — Pathway & Network Analysis](#bioinformatics--pathway--network-analysis)\n- [Bioinformatics — Single-Cell & Spatial Omics](#bioinformatics--single-cell--spatial-omics)\n- [Bioinformatics — Epigenomics & Chromatin](#bioinformatics--epigenomics--chromatin)\n- [Bioinformatics — Metagenomics & Microbiome](#bioinformatics--metagenomics--microbiome)\n- [Bioinformatics — Immunoinformatics & Flow Cytometry](#bioinformatics--immunoinformatics--flow-cytometry)\n- [Bioinformatics — Multi-Omics Integration](#bioinformatics--multi-omics-integration)\n- [Bioinformatics — Proteomics & Metabolomics](#bioinformatics--proteomics--metabolomics)\n- [Bioinformatics — Structural Biology & Cheminformatics](#bioinformatics--structural-biology--cheminformatics)\n- [Bioinformatics — Epidemiological & Causal Genomics](#bioinformatics--epidemiological--causal-genomics)\n\n### Omics & Computational Biology\n- [Single-Cell & Spatial Omics](#single-cell--spatial-omics)\n- [Single-Cell & Trajectory Analysis](#single-cell--trajectory-analysis)\n- [Proteomics & Mass Spectrometry](#proteomics--mass-spectrometry)\n- [Cheminformatics & Drug Discovery](#cheminformatics--drug-discovery)\n- [Protein Structure & Design](#protein-structure--design)\n- [Phylogenetics & Network Analysis](#phylogenetics--network-analysis)\n\n### ClawBio Pipelines\n- [Bioinformatics Orchestration & Pipelines (ClawBio)](#bioinformatics-orchestration--pipelines-clawbio)\n- [Genomics, Ancestry & Pharmacogenomics (ClawBio)](#genomics-ancestry--pharmacogenomics-clawbio)\n- [Structural Biology & Literature (ClawBio)](#structural-biology--literature-clawbio)\n\n### BioOS Extended Suite\n- [BioOS Extended Bioinformatics Suite](#bioos-extended-bioinformatics-suite-mdbabumiamssmllms-universal-life-science-and-clinical-skills-)\n- [Oncology & Precision Medicine Agents (BioOS)](#oncology--precision-medicine-agents-bioos)\n- [Hematology & Blood Disorders (BioOS)](#hematology--blood-disorders-bioos)\n- [Immunology & Cell Therapy (BioOS)](#immunology--cell-therapy-bioos)\n- [Single-Cell & Spatial Agents (BioOS)](#single-cell--spatial-agents-bioos)\n- [Drug Discovery & Design (BioOS)](#drug-discovery--design-bioos)\n- [Clinical AI & Healthcare (BioOS)](#clinical-ai--healthcare-bioos)\n- [Research Infrastructure & Agents (BioOS)](#research-infrastructure--agents-bioos)\n\n### Data Science & Tools\n- [Statistics & Data Analysis](#statistics--data-analysis)\n- [Data Processing & Scientific Computing](#data-processing--scientific-computing)\n- [Scientific Visualization & Communication](#scientific-visualization--communication)\n- [Public Health & Time Series](#public-health--time-series)\n- [Computational Simulation & Ontology](#computational-simulation--ontology-heshamfsmaterials-simulation-skills)\n- [Analyst Personas](#analyst-personas)\n- [Lab Automation & Integration](#lab-automation--integration)\n- [Scientific Research & Writing](#scientific-research--writing)\n- [Scientific Literature & Reference Management](#scientific-literature--reference-management)\n- [Additional Scientific Tools](#additional-scientific-tools)\n- [Developer Workflow Skills](#developer-workflow-skills-obrasuperpowers)\n\n---\n\n## Skills List\n\n## 🧰 General & Core\n\n\u003Cdetails open>\n\u003Csummary>Expand\u002FCollapse this category\u003C\u002Fsummary>\n\n### General Tools\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [agent-browser](skills\u002Fagent-browser\u002F) | 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. |\n| [find-skills](skills\u002Ffind-skills\u002F) | Helps users discover and install agent skills when they ask questions like \"how do I do X\", \"find a skill for X\", \"is there a skill that can...\", or express interest in extending capabilities. |\n| [multi-search-engine](skills\u002Fmulti-search-engine\u002F) | Multi search engine integration with 17 engines (8 CN + 9 Global). Supports Baidu, Bing, 360, Sogou, WeChat, Google, DuckDuckGo, WolframAlpha and more. Supports advanced operators, time filters, site search. No API keys required. |\n| [wikipedia-search](skills\u002Fwikipedia-search\u002F) | Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information. Supports multi-language queries. |\n| [deep-research](skills\u002Fdeep-research\u002F) | Execute autonomous multi-step deep research on any topic. Searches multiple sources, reads full content, synthesizes findings, and produces a structured report. Use for comprehensive research, literature reviews, competitive analysis, or topic deep-dives. |\n| [pdf](skills\u002Fpdf\u002F) | Comprehensive PDF toolkit — extract text and tables, create new PDFs, merge\u002Fsplit documents, handle forms, OCR scanned PDFs. Use when working with any .pdf file. |\n| [docx](skills\u002Fdocx\u002F) | Create, edit, and analyze Word documents (.docx). Supports tracked changes, comments, formatting preservation, and text extraction. Use for drafting, redlining, or extracting content from Word files. |\n| [xlsx](skills\u002Fxlsx\u002F) | Spreadsheet creation, editing, and analysis. Supports formulas, formatting, data analysis, and visualization. Use for any .xlsx, .xlsm, .csv, or .tsv task. |\n| [pptx](skills\u002Fpptx\u002F) | Presentation creation, editing, and analysis. Supports layouts, speaker notes, templates, and design. Use for any .pptx file. |\n| [doc-coauthoring](skills\u002Fdoc-coauthoring\u002F) | Guide users through a structured workflow for co-authoring documentation. Use when writing documentation, proposals, technical specs, decision docs, or similar structured content. |\n\n\u003C\u002Fdetails>\n\n\u003C\u002Fdetails>\n\n## 🏥 Medical & Clinical\n\n\u003Cdetails open>\n\u003Csummary>Expand\u002FCollapse this category\u003C\u002Fsummary>\n\n### Medical Tools\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [pubmed-search](skills\u002Fpubmed-search\u002F) | Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. |\n| [medical-research-toolkit](skills\u002Fmedical-research-toolkit\u002F) | Query 14+ biomedical databases for drug repurposing, target discovery, clinical trials, and literature research. Access ChEMBL, PubMed, ClinicalTrials.gov, OpenTargets, OpenFDA, OMIM, Reactome, KEGG, UniProt, and more through a unified MCP endpoint. |\n| [medical-specialty-briefs](skills\u002Fmedical-specialty-briefs\u002F) | Generate daily or on-demand medical research briefs for any medical specialty. Searches latest research from top-tier journals (NEJM, Lancet, JAMA, BMJ, Nature Medicine), delivers concise summaries with 1-sentence takeaways and direct links. Use when user asks for medical news, research updates, or specialty-specific updates (endocrinology, cardiology, oncology, neurology, etc.). |\n| [usmle](skills\u002Fusmle\u002F) | Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning. Covers Step 1\u002F2 CK\u002FStep 3, IMG-specific guidance, score prediction, and wellbeing support. |\n| [medical-entity-extractor](skills\u002Fmedical-entity-extractor\u002F) | Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages. |\n| [patiently-ai](skills\u002Fpatiently-ai\u002F) | Simplifies medical documents for patients. Takes doctor's letters, test results, prescriptions, discharge summaries, and clinical notes and explains them in clear, personalised language. |\n| [biomedical-search](skills\u002Fbiomedical-search\u002F) | Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels. Powered by Valyu semantic search. |\n| [medical-imaging-review](skills\u002Fmedical-imaging-review\u002F) | Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on imaging topics. |\n| [fhir-developer-skill](skills\u002Ffhir-developer-skill\u002F) | FHIR API development guide for building healthcare endpoints (Patient, Observation, Encounter, Condition, MedicationRequest). Use when developing or integrating FHIR REST APIs. |\n| [clinical-trial-protocol-skill](skills\u002Fclinical-trial-protocol-skill\u002F) | Generate clinical trial protocols for medical devices or drugs. Use when designing clinical studies, creating FDA submission documentation, or developing protocols for investigational products. |\n| [prior-auth-review-skill](skills\u002Fprior-auth-review-skill\u002F) | Automate payer review of prior authorization (PA) requests. Assesses medical necessity, validates against coverage policies, and generates PA decisions. |\n| [clinical-reports](skills\u002Fclinical-reports\u002F) | Write comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology\u002Fpathology\u002Flab), clinical trial reports (ICH-E3, CSR), and patient documentation (SOAP, H&P, discharge summaries). HIPAA\u002FFDA\u002FICH-GCP compliant. |\n| [clinicaltrials-database](skills\u002Fclinicaltrials-database\u002F) | Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data for clinical research and patient matching. |\n| [clinical-decision-support](skills\u002Fclinical-decision-support\u002F) | Generate clinical decision support (CDS) documents for pharmaceutical and clinical research — patient cohort analyses, treatment recommendation reports with GRADE evidence grading, biomarker integration, and statistical outputs (hazard ratios, survival curves). |\n| [tooluniverse-clinical-trial-design](skills\u002Ftooluniverse-clinical-trial-design\u002F) | Strategic clinical trial design feasibility assessment. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Use when planning Phase 1\u002F2 trials or assessing trial feasibility. |\n| [tooluniverse-disease-research](skills\u002Ftooluniverse-disease-research\u002F) | Generate comprehensive disease research reports covering epidemiology, mechanisms, diagnostics, treatments, and ongoing trials. Use when asking about diseases, syndromes, or needing systematic disease analysis. |\n| [tooluniverse-literature-deep-research](skills\u002Ftooluniverse-literature-deep-research\u002F) | Deep literature research with target disambiguation, evidence grading, and structured theme extraction. Resolves gene\u002Fprotein IDs, identifies synonyms, synthesizes biological models, and generates testable hypotheses. Use for thorough literature reviews or target profiles. |\n| [tooluniverse-clinical-guidelines](skills\u002Ftooluniverse-clinical-guidelines\u002F) | Search and retrieve clinical practice guidelines from 12+ sources (NICE, WHO, ADA, AHA\u002FACC, NCCN, SIGN, CPIC, etc.). Covers cardiology, oncology, diabetes, pharmacogenomics, and more. Use when asking about treatment recommendations or standard of care. |\n| [tooluniverse-drug-research](skills\u002Ftooluniverse-drug-research\u002F) | Comprehensive drug research reports covering identity, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET. Use for drug profiling, safety assessment, or clinical development research. |\n| [tooluniverse-drug-repurposing](skills\u002Ftooluniverse-drug-repurposing\u002F) | Identify drug repurposing candidates using target-based, compound-based, and disease-driven strategies. Finds new indications for approved drugs by analyzing targets, bioactivity, and safety profiles. |\n| [tooluniverse-drug-drug-interaction](skills\u002Ftooluniverse-drug-drug-interaction\u002F) | Drug-drug interaction prediction and risk assessment. Analyzes CYP450\u002Ftransporter mechanisms, severity classification, and provides management strategies. Supports polypharmacy analysis (3+ drugs) and alternative drug recommendations. |\n| [tooluniverse-rare-disease-diagnosis](skills\u002Ftooluniverse-rare-disease-diagnosis\u002F) | Differential diagnosis for rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet\u002FOMIM, and interprets variants of uncertain significance. |\n| [tooluniverse-pharmacovigilance](skills\u002Ftooluniverse-pharmacovigilance\u002F) | Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates PRR\u002FROR, identifies serious adverse events, and assesses pharmacogenomic risk. |\n| [tooluniverse-clinical-trial-matching](skills\u002Ftooluniverse-clinical-trial-matching\u002F) | Patient-to-trial matching for precision medicine and oncology. Ranks trials from ClinicalTrials.gov by molecular eligibility, clinical criteria, biomarker alignment, and geographic feasibility with a quantitative Trial Match Score (0-100). |\n| [literature-review](skills\u002Fliterature-review\u002F) | Systematic literature reviews across multiple databases (PubMed, arXiv, bioRxiv, Semantic Scholar). Produces professionally formatted reports with verified citations in APA, Nature, Vancouver styles. |\n| [tooluniverse-precision-oncology](skills\u002Ftooluniverse-precision-oncology\u002F) | Actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, clinical trials, and resistance mechanisms. |\n| [tooluniverse-cancer-variant-interpretation](skills\u002Ftooluniverse-cancer-variant-interpretation\u002F) | Clinical interpretation of somatic mutations in cancer. Given gene+variant (e.g., EGFR L858R, BRAF V600E), assesses oncogenicity, therapeutic implications, and trial eligibility. |\n| [tooluniverse-variant-analysis](skills\u002Ftooluniverse-variant-analysis\u002F) | Production-ready VCF processing, variant annotation, and mutation analysis. Parses VCF files, annotates with ClinVar\u002FgnomAD\u002FCOSMIC, and interprets clinical significance. |\n| [tooluniverse-variant-interpretation](skills\u002Ftooluniverse-variant-interpretation\u002F) | Systematic clinical variant interpretation from raw calls to ACMG-classified recommendations. Aggregates evidence from ClinVar, gnomAD, literature, and population databases. |\n| [tooluniverse-structural-variant-analysis](skills\u002Ftooluniverse-structural-variant-analysis\u002F) | Comprehensive structural variant (SV\u002FCNV) analysis for clinical genomics. Classifies SVs, assesses pathogenicity, and interprets copy number alterations. |\n| [tooluniverse-polygenic-risk-score](skills\u002Ftooluniverse-polygenic-risk-score\u002F) | Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles and interprets PRS percentiles. |\n| [tooluniverse-precision-medicine-stratification](skills\u002Ftooluniverse-precision-medicine-stratification\u002F) | Patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Identifies treatment-relevant subgroups and biomarker-driven therapy options. |\n| [tooluniverse-gwas-trait-to-gene](skills\u002Ftooluniverse-gwas-trait-to-gene\u002F) | Discover genes associated with diseases and traits using GWAS Catalog (500k+ associations) and Open Targets Genetics locus-to-gene predictions. |\n| [tooluniverse-gwas-drug-discovery](skills\u002Ftooluniverse-gwas-drug-discovery\u002F) | Transform GWAS signals into drug targets and repurposing opportunities. Performs locus-to-gene mapping, druggability assessment, and existing drug identification. |\n| [tooluniverse-gwas-study-explorer](skills\u002Ftooluniverse-gwas-study-explorer\u002F) | Compare GWAS studies and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics for cross-study meta-analysis. |\n| [tooluniverse-gwas-finemapping](skills\u002Ftooluniverse-gwas-finemapping\u002F) | Identify and prioritize causal variants at GWAS loci using statistical fine-mapping. Computes posterior probabilities and credible sets for causal variant identification. |\n| [tooluniverse-gwas-snp-interpretation](skills\u002Ftooluniverse-gwas-snp-interpretation\u002F) | Interpret SNPs from GWAS studies by aggregating evidence from GWAS Catalog, Open Targets Genetics, and ClinVar. Retrieves variant-trait associations and functional annotations. |\n| [tooluniverse-phylogenetics](skills\u002Ftooluniverse-phylogenetics\u002F) | Phylogenetics and sequence analysis — alignment processing, evolutionary tree construction, and evolutionary metrics for pathogens or species. |\n| [tooluniverse-epigenomics](skills\u002Ftooluniverse-epigenomics\u002F) | Epigenomics data processing — methylation array analysis (CpG filtering, differential methylation), chromatin accessibility, and histone modification analysis. |\n| [tooluniverse-rnaseq-deseq2](skills\u002Ftooluniverse-rnaseq-deseq2\u002F) | RNA-seq differential expression analysis using PyDESeq2. Performs normalization, dispersion estimation, Wald testing, LFC shrinkage, and pathway enrichment. |\n| [tooluniverse-single-cell](skills\u002Ftooluniverse-single-cell\u002F) | Single-cell RNA-seq analysis using scanpy. Performs QC, normalization, PCA, UMAP, Leiden clustering, trajectory analysis, and cell type annotation. |\n| [tooluniverse-spatial-transcriptomics](skills\u002Ftooluniverse-spatial-transcriptomics\u002F) | Spatial transcriptomics data analysis — maps gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, and Slide-seq platforms. |\n| [tooluniverse-spatial-omics-analysis](skills\u002Ftooluniverse-spatial-omics-analysis\u002F) | Computational analysis for spatial multi-omics data integration — spatially variable genes, domain annotation, and tissue-resolved omics. |\n| [tooluniverse-proteomics-analysis](skills\u002Ftooluniverse-proteomics-analysis\u002F) | Mass spectrometry proteomics analysis — protein quantification, differential expression, PTMs, and protein-protein interaction network construction. |\n| [tooluniverse-metabolomics](skills\u002Ftooluniverse-metabolomics\u002F) | Metabolomics research — identifies metabolites and searches databases (HMDB 220k+ metabolites, MetaboLights, Metabolomics Workbench). |\n| [tooluniverse-metabolomics-analysis](skills\u002Ftooluniverse-metabolomics-analysis\u002F) | Metabolomics data analysis — metabolite identification, quantification, pathway analysis, and metabolic flux from LC-MS, GC-MS, or NMR data. |\n| [tooluniverse-multi-omics-integration](skills\u002Ftooluniverse-multi-omics-integration\u002F) | Integrate transcriptomics, proteomics, epigenomics, genomics, and metabolomics for systems biology and precision medicine. |\n| [tooluniverse-multiomic-disease-characterization](skills\u002Ftooluniverse-multiomic-disease-characterization\u002F) | Systems-level disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers. |\n| [tooluniverse-expression-data-retrieval](skills\u002Ftooluniverse-expression-data-retrieval\u002F) | Retrieve gene expression and omics datasets from ArrayExpress and BioStudies with quality assessment and structured reports. |\n| [tooluniverse-gene-enrichment](skills\u002Ftooluniverse-gene-enrichment\u002F) | Gene enrichment and pathway analysis using gseapy, PANTHER, STRING, Reactome. Supports GO enrichment, KEGG pathways, and 40+ ToolUniverse tools. |\n| [tooluniverse-systems-biology](skills\u002Ftooluniverse-systems-biology\u002F) | Systems biology and pathway analysis using Reactome, KEGG, WikiPathways, Pathway Commons, and BioModels. Network modeling and pathway simulation. |\n| [tooluniverse-protein-interactions](skills\u002Ftooluniverse-protein-interactions\u002F) | Protein-protein interaction network analysis using STRING, BioGRID, and SASBDB. Maps interaction networks with confidence scores and functional modules. |\n| [tooluniverse-protein-structure-retrieval](skills\u002Ftooluniverse-protein-structure-retrieval\u002F) | Retrieve protein structure data from RCSB PDB, PDBe, and AlphaFold with quality assessment and comprehensive structural profiles. |\n| [tooluniverse-protein-therapeutic-design](skills\u002Ftooluniverse-protein-therapeutic-design\u002F) | Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design — RFdiffusion, ProteinMPNN, and ESM. |\n| [tooluniverse-antibody-engineering](skills\u002Ftooluniverse-antibody-engineering\u002F) | Antibody engineering and optimization for therapeutics — humanization, affinity maturation, developability assessment, and immunogenicity prediction. |\n| [tooluniverse-immune-repertoire-analysis](skills\u002Ftooluniverse-immune-repertoire-analysis\u002F) | TCR\u002FBCR repertoire analysis from sequencing data — clonality, diversity, V(D)J gene usage, clonal expansion, and antigen specificity prediction. |\n| [tooluniverse-immunotherapy-response-prediction](skills\u002Ftooluniverse-immunotherapy-response-prediction\u002F) | Predict patient response to immune checkpoint inhibitors using multi-biomarker integration — TMB, MSI, PD-L1, TIL signatures, and HLA typing. |\n| [tooluniverse-infectious-disease](skills\u002Ftooluniverse-infectious-disease\u002F) | Pathogen characterization and drug repurposing for infectious disease outbreaks. Identifies taxonomy, essential proteins, structural targets, and treatment options. |\n| [tooluniverse-crispr-screen-analysis](skills\u002Ftooluniverse-crispr-screen-analysis\u002F) | CRISPR screen analysis for functional genomics — pooled or arrayed screens (knockout\u002Factivation\u002Finterference) to identify essential genes and hits. |\n| [tooluniverse-target-research](skills\u002Ftooluniverse-target-research\u002F) | Comprehensive biological target intelligence — protein info, structure, interactions, pathways, expression, variant landscape, and drug pipeline. |\n| [tooluniverse-network-pharmacology](skills\u002Ftooluniverse-network-pharmacology\u002F) | Compound-target-disease network analysis for drug repurposing, polypharmacology discovery, and systems pharmacology. |\n| [tooluniverse-statistical-modeling](skills\u002Ftooluniverse-statistical-modeling\u002F) | Statistical modeling on biomedical datasets — linear\u002Flogistic regression, mixed-effects models, survival analysis, and Bayesian methods. |\n| [tooluniverse-image-analysis](skills\u002Ftooluniverse-image-analysis\u002F) | Biomedical microscopy image analysis — colony morphometry, cell counting, fluorescence quantification, and statistical comparison of imaging data. |\n| [literature-search](skills\u002Fliterature-search\u002F) | Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv using natural language queries powered by Valyu semantic search. |\n| [medrxiv-search](skills\u002Fmedrxiv-search\u002F) | Search medRxiv medical preprints with natural language queries powered by Valyu semantic search. |\n| [clinical-trials-search](skills\u002Fclinical-trials-search\u002F) | Search ClinicalTrials.gov with natural language queries — find trials by condition, enrollment status, and outcomes via Valyu. |\n| [drug-discovery-search](skills\u002Fdrug-discovery-search\u002F) | End-to-end drug discovery platform combining ChEMBL, DrugBank, targets, and FDA labels via natural language Valyu search. |\n| [drug-labels-search](skills\u002Fdrug-labels-search\u002F) | Search FDA drug labels with natural language queries — indications, dosing, and safety data via Valyu. |\n| [chembl-search](skills\u002Fchembl-search\u002F) | Search ChEMBL bioactive molecules database — compounds, assay data, and bioactivity via Valyu semantic search. |\n| [open-targets-search](skills\u002Fopen-targets-search\u002F) | Search Open Targets drug-disease associations and target validation via Valyu semantic search. |\n| [patents-search](skills\u002Fpatents-search\u002F) | Search global patents with natural language queries — prior art, patent landscapes, and innovation tracking via Valyu. |\n| [drugbank-search](skills\u002Fdrugbank-search\u002F) | Search DrugBank comprehensive drug database — mechanisms, interactions, and safety data via Valyu semantic search. |\n| [arxiv-search](skills\u002Farxiv-search\u002F) | Search arXiv preprints (biology, medicine, AI) using natural language queries powered by Valyu semantic search. |\n| [gwas-database](skills\u002Fgwas-database\u002F) | Query NHGRI-EBI GWAS Catalog for SNP-trait associations by rs ID, disease\u002Ftrait, or gene. Retrieve p-values and summary statistics for genetic epidemiology. |\n| [scikit-survival](skills\u002Fscikit-survival\u002F) | Survival analysis and time-to-event modeling in Python — Kaplan-Meier, Cox regression, log-rank tests, and censored data handling using scikit-survival. |\n\n\u003C\u002Fdetails>\n\n### Drug Safety & Chemical Biology\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [tooluniverse-adverse-event-detection](skills\u002Ftooluniverse-adverse-event-detection\u002F) | Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100). |\n| [tooluniverse-binder-discovery](skills\u002Ftooluniverse-binder-discovery\u002F) | Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. |\n| [tooluniverse-chemical-compound-retrieval](skills\u002Ftooluniverse-chemical-compound-retrieval\u002F) | Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Comprehensive compound profiles with identifiers, properties, bioactivity. |\n| [tooluniverse-chemical-safety](skills\u002Ftooluniverse-chemical-safety\u002F) | Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and STITCH chemical-protein interactions. |\n| [tooluniverse-drug-target-validation](skills\u002Ftooluniverse-drug-target-validation\u002F) | Computational validation of drug targets across 10 dimensions: disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, and expression evidence. |\n| [tooluniverse-sequence-retrieval](skills\u002Ftooluniverse-sequence-retrieval\u002F) | Retrieve biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. |\n\n\u003C\u002Fdetails>\n\n### Medical Imaging & Pathology\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [pydicom](skills\u002Fpydicom\u002F) | Python library for working with DICOM medical imaging files. Reading, writing, modifying DICOM data, extracting pixel data, handling metadata and multi-frame files. |\n| [histolab](skills\u002Fhistolab\u002F) | Digital pathology image processing toolkit for whole slide images (WSI). Process H&E or IHC stained tissue images, extract tiles from gigapixel slides. |\n| [pathml](skills\u002Fpathml\u002F) | Computational pathology toolkit for analyzing WSI and multiparametric imaging data. H&E stained images, multiplex immunofluorescence, spatial omics integration. |\n| [omero-integration](skills\u002Fomero-integration\u002F) | Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs\u002Fannotations, for high-content screening workflows. |\n| [neurokit2](skills\u002Fneurokit2\u002F) | Comprehensive biosignal processing: ECG, EEG, EDA, RSP, PPG, EMG, EOG signals. Cardiovascular signal analysis, neurophysiology, and physiological data processing. |\n| [neuropixels-analysis](skills\u002Fneuropixels-analysis\u002F) | Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, for neuroscience research. |\n\n\u003C\u002Fdetails>\n\n### Healthcare ML & Clinical AI\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [pyhealth](skills\u002Fpyhealth\u002F) | Comprehensive healthcare AI toolkit for developing ML models with clinical data (EHR, claims). Task definition API, model training, evaluation for clinical NLP and prediction. |\n| [scikit-learn](skills\u002Fscikit-learn\u002F) | Machine learning in Python: supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning. |\n| [transformers](skills\u002Ftransformers\u002F) | Pre-trained transformer models for NLP, computer vision, audio, and multimodal tasks. Text generation, classification, question answering, and biomedical NLP (BioBERT, ClinicalBERT). |\n| [shap](skills\u002Fshap\u002F) | Model interpretability using SHAP (SHapley Additive exPlanations). Explain ML model predictions, compute feature importance, generate SHAP plots for biomedical models. |\n| [umap-learn](skills\u002Fumap-learn\u002F) | UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D\u002F3D visualization, clustering preprocessing (HDBSCAN), for high-dimensional omics data. |\n\n\u003C\u002Fdetails>\n\n### Health & Wellness Analytics\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [nutrition-analyzer](skills\u002Fnutrition-analyzer\u002F) | Comprehensive nutrition analysis: macro\u002Fmicronutrient tracking, dietary assessment, meal planning, food data lookup, and nutritional recommendations. |\n| [mental-health-analyzer](skills\u002Fmental-health-analyzer\u002F) | Mental health data analysis: mood tracking, symptom patterns, PHQ\u002FGAD scoring, behavioral insights, and wellness recommendations. |\n| [sleep-analyzer](skills\u002Fsleep-analyzer\u002F) | Sleep quality analysis: sleep stages, duration, efficiency metrics, circadian rhythm assessment, and sleep hygiene recommendations. |\n| [rehabilitation-analyzer](skills\u002Frehabilitation-analyzer\u002F) | Rehabilitation progress tracking: functional assessments, exercise programs, recovery milestones, and outcome measurement for physical\u002Foccupational therapy. |\n| [fitness-analyzer](skills\u002Ffitness-analyzer\u002F) | Fitness performance analysis: exercise tracking, strength\u002Fcardio metrics, training load, VO2max estimation, and periodization planning. |\n| [health-trend-analyzer](skills\u002Fhealth-trend-analyzer\u002F) | Longitudinal health trend analysis: vital sign tracking, biomarker trends, risk factor monitoring, and predictive health insights. |\n| [weightloss-analyzer](skills\u002Fweightloss-analyzer\u002F) | Weight management analytics: caloric balance, body composition tracking, progress monitoring, and evidence-based weight loss strategies. |\n| [goal-analyzer](skills\u002Fgoal-analyzer\u002F) | Health goal tracking and analysis: SMART goal setting, progress metrics, habit formation, and motivational insights for wellness objectives. |\n| [occupational-health-analyzer](skills\u002Foccupational-health-analyzer\u002F) | Occupational health assessment: workplace ergonomics, exposure risk, work-related illness surveillance, and return-to-work planning. |\n| [travel-health-analyzer](skills\u002Ftravel-health-analyzer\u002F) | Travel medicine: destination health risks, vaccination requirements, malaria prophylaxis, altitude sickness, and traveler health preparation. |\n| [family-health-analyzer](skills\u002Ffamily-health-analyzer\u002F) | Family health management: pediatric milestones, family medical history, preventive screening schedules, and multigenerational health tracking. |\n| [tcm-constitution-analyzer](skills\u002Ftcm-constitution-analyzer\u002F) | Traditional Chinese Medicine constitution analysis: TCM body type assessment, pattern differentiation, herbal recommendations, and lifestyle guidance. |\n| [emergency-card](skills\u002Femergency-card\u002F) | Generate emergency medical information cards with critical health data, medications, allergies, and emergency contacts for patient safety. |\n| [ai-analyzer](skills\u002Fai-analyzer\u002F) | AI-powered comprehensive health data interpretation combining multiple biomarkers and health metrics for holistic wellness assessment. |\n| [wellally-tech](skills\u002Fwellally-tech\u002F) | Technical framework for WellAlly health analytics platform: integration patterns, data pipelines, and health AI infrastructure. |\n\n\u003C\u002Fdetails>\n\n### Mental Health & Crisis Intervention\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [crisis-detection-intervention-ai](skills\u002Fcrisis-detection-intervention-ai\u002F) | Detect crisis signals using NLP and mental health sentiment analysis. Implements suicide ideation detection, automated escalation, and crisis resource integration for mental health apps and recovery platforms. |\n| [crisis-response-protocol](skills\u002Fcrisis-response-protocol\u002F) | Handle mental health crisis situations safely: crisis detection, safety protocols, emergency escalation, suicide prevention, and hotline integration for AI coaching applications. |\n| [hipaa-compliance](skills\u002Fhipaa-compliance\u002F) | Ensure HIPAA compliance when handling PHI. Audit logging, data access controls, security event tracking, and compliance verification for health data applications. |\n| [clinical-diagnostic-reasoning](skills\u002Fclinical-diagnostic-reasoning\u002F) | Identify and counteract cognitive biases in medical decision-making through systematic error analysis, differential diagnosis frameworks, and clinical judgment improvement. |\n| [speech-pathology-ai](skills\u002Fspeech-pathology-ai\u002F) | AI-powered speech-language pathology: phoneme analysis, articulation visualization, voice disorder assessment, fluency intervention, AAC, and stuttering treatment support. |\n| [hrv-alexithymia-expert](skills\u002Fhrv-alexithymia-expert\u002F) | Heart rate variability biometrics and emotional awareness training. HRV analysis, interoception training, biofeedback, vagal tone assessment, and autonomic nervous system evaluation. |\n| [adhd-daily-planner](skills\u002Fadhd-daily-planner\u002F) | ADHD-optimized daily planning: time-blind friendly scheduling, executive function support, dopamine-aware task design, and neurodivergent-friendly productivity systems. |\n| [grief-companion](skills\u002Fgrief-companion\u002F) | Compassionate bereavement support, memorial creation, grief education, and healing journey guidance through the non-linear path of loss. |\n| [jungian-psychologist](skills\u002Fjungian-psychologist\u002F) | Jungian analytical psychology: shadow work, archetypal analysis, dream interpretation, active imagination, addiction\u002Frecovery through depth psychology lens, and individuation process. |\n| [modern-drug-rehab-computer](skills\u002Fmodern-drug-rehab-computer\u002F) | Comprehensive addiction recovery knowledge system: evidence-based treatment (CBT, DBT, MI, EMDR, MAT), recovery resources, crisis intervention, and family systems for rehab environments. |\n| [recovery-community-moderator](skills\u002Frecovery-community-moderator\u002F) | Trauma-informed AI moderation for addiction recovery communities: harm reduction, 12-step traditions, conflict detection, and crisis post identification. |\n\n\u003C\u002Fdetails>\n\n### Medical Device & Regulatory\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [iso-13485-certification](skills\u002Fiso-13485-certification\u002F) | Comprehensive toolkit for ISO 13485 QMS documentation for medical devices: gap analysis, Quality Manuals, procedures, Medical Device Files. Covers FDA QMSR, EU MDR compliance. |\n\n\u003C\u002Fdetails>\n\n\u003C\u002Fdetails>\n\n## 🗂️ Scientific Databases\n\n\u003Cdetails open>\n\u003Csummary>Expand\u002FCollapse this category\u003C\u002Fsummary>\n\n### Scientific Databases (Genomics & Variants)\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [clinvar-database](skills\u002Fclinvar-database\u002F) | Query NCBI ClinVar for variant clinical significance. Search by gene\u002Fposition, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine. |\n| [clinpgx-database](skills\u002Fclinpgx-database\u002F) | Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions. |\n| [cosmic-database](skills\u002Fcosmic-database\u002F) | Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication. |\n| [ensembl-database](skills\u002Fensembl-database\u002F) | Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research. |\n| [gene-database](skills\u002Fgene-database\u002F) | Query NCBI Gene via E-utilities\u002FDatasets API. Search by symbol\u002FID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis. |\n| [geo-database](skills\u002Fgeo-database\u002F) | Access NCBI GEO for gene expression\u002Fgenomics data. Search\u002Fdownload microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT\u002FMatrix files, for transcriptomics and expression analysis. |\n| [ena-database](skills\u002Fena-database\u002F) | Access European Nucleotide Archive via API\u002FFTP. Retrieve DNA\u002FRNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. |\n| [gget](skills\u002Fgget\u002F) | CLI\u002FPython toolkit for rapid bioinformatics queries with access to 20+ databases: Ensembl, UniProt, AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, BLAST, and more. |\n| [pysam](skills\u002Fpysam\u002F) | Genomic file toolkit. Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignments, VCF\u002FBCF variants, FASTA\u002FFASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines. |\n\n\u003C\u002Fdetails>\n\n### Scientific Databases (Proteins, Pathways & Drugs)\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [alphafold-database](skills\u002Falphafold-database\u002F) | Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB\u002FmmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology. |\n| [pdb-database](skills\u002Fpdb-database\u002F) | Access RCSB PDB for 3D protein\u002Fnucleic acid structures. Search by text\u002Fsequence\u002Fstructure, download coordinates (PDB\u002FmmCIF), retrieve metadata, for structural biology and drug discovery. |\n| [uniprot-database](skills\u002Funiprot-database\u002F) | Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot\u002FTrEMBL. For multi-database workflows, prefer bioservices (unified interface to 40+ services). |\n| [string-database](skills\u002Fstring-database\u002F) | Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO\u002FKEGG enrichment, interaction discovery, 5000+ species, for systems biology. |\n| [kegg-database](skills\u002Fkegg-database\u002F) | Direct REST API access to KEGG (academic use). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. |\n| [reactome-database](skills\u002Freactome-database\u002F) | Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology. |\n| [brenda-database](skills\u002Fbrenda-database\u002F) | Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, substrate-specific enzyme info for biochemical research. |\n| [hmdb-database](skills\u002Fhmdb-database\u002F) | Access Human Metabolome Database (220K+ metabolites). Search by name\u002FID\u002Fstructure, retrieve chemical properties, biomarker data, NMR\u002FMS spectra, pathways, for metabolomics. |\n| [metabolomics-workbench-database](skills\u002Fmetabolomics-workbench-database\u002F) | Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS\u002FNMR data, m\u002Fz searches, for metabolomics and biomarker discovery. |\n| [pubchem-database](skills\u002Fpubchem-database\u002F) | Query PubChem via PUG-REST API (110M+ compounds). Search by name\u002FCID\u002FSMILES, retrieve properties, similarity\u002Fsubstructure searches, bioactivity, for cheminformatics. |\n| [chembl-database](skills\u002Fchembl-database\u002F) | Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure\u002Fproperties, retrieve bioactivity data (IC50, Ki), find inhibitors, for medicinal chemistry. |\n| [drugbank-database](skills\u002Fdrugbank-database\u002F) | Access comprehensive drug information from DrugBank including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. |\n| [zinc-database](skills\u002Fzinc-database\u002F) | Access ZINC (230M+ purchasable compounds). Search by ZINC ID\u002FSMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening. |\n| [opentargets-database](skills\u002Fopentargets-database\u002F) | Query Open Targets Platform for target-disease associations, drug target discovery, tractability\u002Fsafety data, genetics\u002Fomics evidence, known drugs, for therapeutic target identification. |\n| [fda-database](skills\u002Ffda-database\u002F) | Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis. |\n| [pubmed-database](skills\u002Fpubmed-database\u002F) | Direct REST API access to PubMed. Advanced Boolean\u002FMeSH queries, E-utilities API, batch processing, citation management. |\n| [openalex-database](skills\u002Fopenalex-database\u002F) | Query and analyze scholarly literature using the OpenAlex database. Search for academic papers, analyze research trends, find works by authors or institutions. |\n| [biorxiv-database](skills\u002Fbiorxiv-database\u002F) | Search bioRxiv preprint server by keywords, authors, date ranges, or categories, retrieving paper metadata for life sciences preprint discovery. |\n| [bioservices](skills\u002Fbioservices\u002F) | Primary Python tool for 40+ bioinformatics services. Unified API for UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO — preferred for multi-database workflows. |\n| [uspto-database](skills\u002Fuspto-database\u002F) | Access USPTO APIs for patent\u002Ftrademark searches, examination history (PEDS), assignments, citations, office actions, for IP analysis and prior art searches. |\n\n\u003C\u002Fdetails>\n\n### Cancer Genomics Databases\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [cbioportal-database](skills\u002Fcbioportal-database\u002F) | Query cBioPortal for cancer genomics: somatic mutations, copy number, gene expression, and survival data across hundreds of cancer studies. Cancer target validation, oncogene analysis, and patient-level genomic profiling. |\n| [depmap](skills\u002Fdepmap\u002F) | Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity, and gene effect profiles. Identify cancer-specific vulnerabilities and synthetic lethal interactions. |\n| [imaging-data-commons](skills\u002Fimaging-data-commons\u002F) | Query and download public cancer imaging data from NCI Imaging Data Commons. Access radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. |\n\n\u003C\u002Fdetails>\n\n### Genomic & Molecular Databases\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [bindingdb-database](skills\u002Fbindingdb-database\u002F) | Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Drug discovery, lead optimization, polypharmacology, and SAR studies. |\n| [gnomad-database](skills\u002Fgnomad-database\u002F) | Query gnomAD for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Variant pathogenicity interpretation and rare disease genetics. |\n| [gtex-database](skills\u002Fgtex-database\u002F) | Query GTEx for tissue-specific gene expression, eQTLs, and sQTLs. Link GWAS variants to gene regulation and interpret non-coding variant effects. |\n| [interpro-database](skills\u002Finterpro-database\u002F) | Query InterPro for protein family, domain, and functional site annotations. Integrates Pfam, PANTHER, PRINTS, SMART, and 11+ databases for protein function prediction. |\n| [jaspar-database](skills\u002Fjaspar-database\u002F) | Query JASPAR for transcription factor binding site profiles (PWMs\u002FPFMs). Regulatory genomics, motif analysis, and GWAS regulatory variant interpretation. |\n| [monarch-database](skills\u002Fmonarch-database\u002F) | Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations. Integrates OMIM, ORPHANET, HPO, ClinVar for rare disease gene discovery. |\n| [tiledbvcf](skills\u002Ftiledbvcf\u002F) | Scalable VCF\u002FBCF ingestion, storage, and parallel queries using TileDB for population genomics at scale. |\n\n\u003C\u002Fdetails>\n\n### Structural Biology & Drug Discovery\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [molecular-dynamics](skills\u002Fmolecular-dynamics\u002F) | Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Protein\u002Fsmall molecule systems, force fields, energy minimization, RMSD\u002FRMSF analysis, free energy surfaces. |\n| [glycoengineering](skills\u002Fglycoengineering\u002F) | Analyze and engineer protein glycosylation. Predict N\u002FO-glycosylation sites, access glycoengineering tools (NetOGlyc, GlycoShield). Therapeutic antibody optimization and vaccine design. |\n| [adaptyv](skills\u002Fadaptyv\u002F) | Cloud laboratory platform for automated protein testing: binding assays, expression testing, thermostability, enzyme activity. Protein sequence optimization with NetSolP, SoluProt, ESM. |\n| [ginkgo-cloud-lab](skills\u002Fginkgo-cloud-lab\u002F) | Submit and manage protocols on Ginkgo Bioworks Cloud Lab for autonomous lab execution. Cell-free protein expression, protocol workflows, and biotech automation. |\n\n\u003C\u002Fdetails>\n\n\u003C\u002Fdetails>\n\n## 🧬 Bioinformatics (gptomics bio-* suite)\n\n\u003Cdetails open>\n\u003Csummary>Expand\u002FCollapse this category\u003C\u002Fsummary>\n\n### Bioinformatics Tools & Pipelines\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [biopython](skills\u002Fbiopython\u002F) | Primary Python toolkit for molecular biology: PubMed\u002FNCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), BLAST workflows. |\n| [scikit-bio](skills\u002Fscikit-bio\u002F) | Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha\u002Fbeta, UniFrac), ordination (PCoA), PERMANOVA, for microbiome analysis. |\n| [etetoolkit](skills\u002Fetetoolkit\u002F) | Phylogenetic tree toolkit (ETE). Tree manipulation (Newick\u002FNHX), evolutionary event detection, orthology\u002Fparalogy, NCBI taxonomy, visualization (PDF\u002FSVG), for phylogenomics. |\n| [deeptools](skills\u002Fdeeptools\u002F) | NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps\u002Fprofiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization. |\n| [nextflow-development](skills\u002Fnextflow-development\u002F) | Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use for RNA-seq, WGS\u002FWES, or ATAC-seq from local FASTQs or public datasets (GEO\u002FSRA). |\n| [fastq-analysis](skills\u002Ffastq-analysis\u002F) | SRA downloading, FASTQ quality control, STAR alignment, gene quantification, and single-cell kallisto\u002Fbustools pipelines for bulk and single-cell sequencing data. |\n| [geniml](skills\u002Fgeniml\u002F) | Genomic interval data (BED files) for machine learning tasks. Train region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis. |\n| [gtars](skills\u002Fgtars\u002F) | High-performance genomic interval analysis in Rust with Python bindings. Genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models. |\n| [arboreto](skills\u002Farboreto\u002F) | Infer gene regulatory networks (GRNs) from gene expression data using GRNBoost2 and GENIE3 algorithms. For bulk RNA-seq and single-cell RNA-seq regulatory network inference. |\n| [lamindb](skills\u002Flamindb\u002F) | Open-source biological data framework for queryable, traceable, reproducible, and FAIR datasets (scRNA-seq, genomics, imaging). |\n| [dnanexus-integration](skills\u002Fdnanexus-integration\u002F) | DNAnexus cloud genomics platform. Build apps\u002Fapplets, manage data, dxpy Python SDK, run workflows, FASTQ\u002FBAM\u002FVCF, for genomics pipeline development. |\n| [latchbio-integration](skills\u002Flatchbio-integration\u002F) | Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow\u002F@task decorators, deploy serverless workflows, Nextflow\u002FSnakemake integration. |\n\n\u003C\u002Fdetails>\n\n### Bioinformatics — Clinical Databases & Variant Analysis\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [bio-clinical-databases-clinvar-lookup](skills\u002Fbio-clinical-databases-clinvar-lookup\u002F) | Query ClinVar for clinical variant classifications, pathogenicity assertions, and review status. |\n| [bio-clinical-databases-dbsnp-queries](skills\u002Fbio-clinical-databases-dbsnp-queries\u002F) | Query dbSNP for SNP frequency, allele, and functional annotation data. |\n| [bio-clinical-databases-gnomad-frequencies](skills\u002Fbio-clinical-databases-gnomad-frequencies\u002F) | Retrieve population allele frequencies from gnomAD for rare variant interpretation. |\n| [bio-clinical-databases-hla-typing](skills\u002Fbio-clinical-databases-hla-typing\u002F) | HLA typing from sequencing data using standard typing tools and databases. |\n| [bio-clinical-databases-myvariant-queries](skills\u002Fbio-clinical-databases-myvariant-queries\u002F) | Batch query MyVariant.info for aggregated variant annotations from multiple databases. |\n| [bio-clinical-databases-pharmacogenomics](skills\u002Fbio-clinical-databases-pharmacogenomics\u002F) | PharmGKB\u002FCPIC pharmacogenomics variant lookup for drug-gene interactions. |\n| [bio-clinical-databases-polygenic-risk](skills\u002Fbio-clinical-databases-polygenic-risk\u002F) | Calculate polygenic risk scores from GWAS summary statistics and genotype data. |\n| [bio-clinical-databases-somatic-signatures](skills\u002Fbio-clinical-databases-somatic-signatures\u002F) | Extract and classify mutational signatures from somatic variant catalogs (COSMIC). |\n| [bio-clinical-databases-tumor-mutational-burden](skills\u002Fbio-clinical-databases-tumor-mutational-burden\u002F) | Compute tumor mutational burden (TMB) from somatic variant calls. |\n| [bio-clinical-databases-variant-prioritization](skills\u002Fbio-clinical-databases-variant-prioritization\u002F) | Rank and filter candidate variants by pathogenicity scores, inheritance, and phenotype match. |\n| [bio-variant-calling](skills\u002Fbio-variant-calling\u002F) | GATK-based germline variant calling pipeline from aligned BAM\u002FCRAM files. |\n| [bio-variant-calling-clinical-interpretation](skills\u002Fbio-variant-calling-clinical-interpretation\u002F) | Interpret variant calls in clinical context with ACMG guidelines. |\n| [bio-variant-calling-deepvariant](skills\u002Fbio-variant-calling-deepvariant\u002F) | DeepVariant deep-learning variant caller for short-read WGS\u002FWES data. |\n| [bio-variant-calling-filtering-best-practices](skills\u002Fbio-variant-calling-filtering-best-practices\u002F) | Apply VQSR and hard-filtering best practices to variant call sets. |\n| [bio-variant-calling-joint-calling](skills\u002Fbio-variant-calling-joint-calling\u002F) | Joint genotyping across multiple samples for improved variant discovery. |\n| [bio-variant-calling-structural-variant-calling](skills\u002Fbio-variant-calling-structural-variant-calling\u002F) | Call structural variants (SVs) from long-read or paired-end sequencing. |\n| [bio-variant-annotation](skills\u002Fbio-variant-annotation\u002F) | Annotate VCF files with functional, population, and clinical consequence data. |\n| [bio-variant-normalization](skills\u002Fbio-variant-normalization\u002F) | Normalize variant representations (left-alignment, decomposition) for consistent comparison. |\n| [bio-vcf-basics](skills\u002Fbio-vcf-basics\u002F) | Read, write, and parse VCF files; filter by quality, region, and sample. |\n| [bio-vcf-manipulation](skills\u002Fbio-vcf-manipulation\u002F) | Advanced VCF manipulation: merging, splitting, reformatting, subset extraction. |\n| [bio-vcf-statistics](skills\u002Fbio-vcf-statistics\u002F) | Compute variant statistics: ts\u002Ftv ratio, heterozygosity, depth distributions. |\n| [bio-gatk-variant-calling](skills\u002Fbio-gatk-variant-calling\u002F) | End-to-end GATK HaplotypeCaller variant calling with BQSR and joint genotyping. |\n| [bio-copy-number-cnv-annotation](skills\u002Fbio-copy-number-cnv-annotation\u002F) | Annotate CNV calls with gene content, database overlap, and clinical significance. |\n| [bio-copy-number-cnv-visualization](skills\u002Fbio-copy-number-cnv-visualization\u002F) | Visualize copy number profiles and segment plots from WGS\u002FWES data. |\n| [bio-copy-number-cnvkit-analysis](skills\u002Fbio-copy-number-cnvkit-analysis\u002F) | CNVKit copy number analysis for targeted sequencing and WES data. |\n| [bio-copy-number-gatk-cnv](skills\u002Fbio-copy-number-gatk-cnv\u002F) | GATK4 somatic copy number alteration calling pipeline. |\n| [bio-tumor-fraction-estimation](skills\u002Fbio-tumor-fraction-estimation\u002F) | Estimate tumor purity and ploidy from allele frequencies and copy number data. |\n| [bio-ctdna-mutation-detection](skills\u002Fbio-ctdna-mutation-detection\u002F) | Detect circulating tumor DNA mutations from liquid biopsy ultra-deep sequencing. |\n| [bio-cfdna-preprocessing](skills\u002Fbio-cfdna-preprocessing\u002F) | Process cell-free DNA sequencing data: adapter trimming, deduplication, QC. |\n| [bio-methylation-based-detection](skills\u002Fbio-methylation-based-detection\u002F) | Detect methylation-based cancer signals from cfDNA methylation data. |\n| [bio-longitudinal-monitoring](skills\u002Fbio-longitudinal-monitoring\u002F) | Track somatic variant evolution and clonal dynamics across serial samples. |\n\n\u003C\u002Fdetails>\n\n### Bioinformatics — Sequencing & Read QC\n\n\u003Cdetails>\n\u003Csummary>Click to expand skill list\u003C\u002Fsummary>\n\n| Skill | Description |\n|-------|-------------|\n| [bio-fastq-quality](skills\u002Fbio-fastq-quality\u002F) | Assess FASTQ read quality with FastQC\u002FMultiQC; generate per-sample QC reports. |\n| [bio-read-qc-adapter-trimming](skills\u002Fbio-read-qc-adapter-trimming\u002F) | Trim sequencing adapters with Trimmomatic, Cutadapt, or fastp. |\n| [bio-read-qc-contamination-screening](skills\u002Fbio-read-qc-contamination-screening\u002F) | Screen reads for human\u002Fmicrobial contamination using FastQ Screen or Kraken. |\n| [bio-read-qc-fastp-workflow](skills\u002Fbio-read-qc-fastp-workflow\u002F) | End-to-end read QC and preprocessing with fastp including UMI handling. |\n| [bio-read-qc-quality-filtering](skills\u002Fbio-read-qc-quality-filtering\u002F) | Apply quality-score and length filters to remove low-quality reads. |\n| [bio-read-qc-quality-reports](skills\u002Fbio-read-qc-quality-reports\u002F) | Aggregate multi-sample QC reports with MultiQC. |\n| [bio-read-qc-umi-processing](skills\u002Fbio-read-qc-umi-processing\u002F) | Deduplicate PCR duplicates using UMI-tools for accurate quantification. |\n| [bio-paired-end-fastq](skills\u002Fbio-paired-end-fastq\u002F) | Handle paired-end FASTQ files: validation, interleaving, splitting. |\n| [bio-alignment-io](skills\u002Fbio-alignment-io\u002F) | Read\u002Fwrite SAM\u002FBAM\u002FCRAM alignment files with pysam and samtools. |\n| [bio-alignment-msa-parsing](skills\u002Fbio-alignment-msa-parsing\u002F) | Parse and analyze multiple sequence alignments (FASTA, ClustalW, Stockholm). |\n| [bio-alignment-msa-statistics](skills\u002Fbio-alignment-msa-statistics\u002F) | Compute MSA statistics: conservation, gap content, entropy. |\n| [bio-alignment-pairwise](skills\u002Fbio-alignment-pairwise\u002F) | Pairwise sequence alignment using Smith-Waterman, Needleman-Wunsch, BLAST. |\n| [bio-longread-alignment](skills\u002Fbio-longread-alignment\u002F) | Align long reads (ONT\u002FPacBio) with minimap2; sort and index BAM files. |\n| [bio-longread-qc](skills\u002Fbio-longread-qc\u002F) | Quality control for long-read sequencing: read length, N50, error rate. |\n| [bio-longread-medaka](skills\u002Fbio-longread-medaka\u002F) | Consensus polishing and variant calling with Oxford Nanopore Medaka. |\n| [bio-longread-structural-variants](skills\u002Fbio-longread-structural-variants\u002F) | Call large structural variants from long-read data with Sniffles\u002FPBSV. |\n| [bio-basecalling](skills\u002Fbio-basecalling\u002F) | Base-call raw ONT signals with Dorado\u002FGuppy; convert FAST5 to FASTQ. |\n| [bio-compressed-files](skills\u002Fbio-compressed-files\u002F) | Handle compressed bioinformatics files: bgzip, tabix, zstd, htslib. |\n| [bio-format-conversion](skills\u002Fbio-format-conversion\u002F) | Convert between bioinformatics formats: FASTQ↔FASTA, BAM↔CRAM, BED↔GTF. |\n| [bio-sequence-statistics](skills\u002Fbio-sequence-statistics\u002F) | Compute sequence statistics: GC content, length distributions, complexity. |\n| [bio-read-sequences](skills\u002Fbio-read-sequences\u002F) | Read and iterate over biological sequences from FASTA\u002FFASTQ files. |\n| [bio-write-sequences](skills\u002Fbio-write-sequences\u002F) | Write biological sequences to FASTA\u002FFASTQ with metadata preservation. |\n| [bio-filter-sequences](skills\u002Fbio-filter-sequences\u002F) | Filter sequences by length, quality, pattern, or taxonomy label. |\n| [bio-batch-processing](skills\u002Fbio-batch-processing\u002F) | Batch-process large bioinformatics datasets across samples and cohorts. |\n| [bio-rnaseq-qc](skills\u002Fbio-rnaseq-qc\u002F) | RNA-seq specific QC: strandedness, rRNA contamination, gene body coverage. |\n| [bio-long-read-sequencing-clair3-variants](skills\u002Fbio-long-read-sequencing-clair3-variants\u002F) | Call variants from long-read sequencing with Clair3 deep-learning model. |\n| [bio-long-read-sequencing-isoseq-analysis](skills\u002Fbio-long-read-sequencing-isoseq-analysis\u002F) | Iso-Seq full-length transcript analysis for isoform discovery. |\n| [bio-long-read-sequencing-nanopore-methylation](skills\u002Fbio-long-read-","OpenClaw Medical Skills 是一个为 OpenClaw 和 NanoClaw 框架设计的开源医疗AI技能库，包含869个精选技能。这些技能覆盖了从临床研究到基因组学、药物发现及生物信息学等多个领域，每个技能都是一个独立模块（`SKILL.md`文件），能够教授AI代理特定领域的知识和工作流程，并连接到真实的数据库、API和计算工具，生成结构化的临床或科学相关输出。该项目适用于需要将通用AI助手转化为强大医学科研伙伴的场景，如医疗机构、科研单位和个人研究人员等。",2,"2026-06-11 03:48:58","high_star"]