Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
A router/index skill over 1,676 deduplicated biomedical AI agent skills, aggregated and
deduplicated from 20 open-source repositories into 15 categories. Each skill is a
self-contained SKILL.md folder compatible with Claude-based agent frameworks.
https://raw.githubusercontent.com/BioTender-max/awesome-bio-agent-skills/main/bioskill_index_v3.csv (1,676 rows)This skill does not duplicate the 1,676 skills. It tells the agent how to (1) search the index, (2) locate the single best-matching skill, (3) fetch that skill's
SKILL.mdon demand, and (4) follow it. Skill bodies are fetched from GitHub on demand; agit clonefallback (incl. a China mirror) is provided for offline / bulk / private use.
When a user asks for any bioinformatics task, workflow, or analysis:
The index has columns: skill_name, folder_name, source_repo, category, description, file_count, archive_path.
archive_path = <source_repo>/<folder_name> and is the path under skills/.
Search skill_name + description + category + source_repo together (descriptions contain
rich "Use when..." text), and present ranked candidates with their category and archive_path
so the right one can be chosen:
import pandas as pd
# Authoritative v3 index. Prefer a local copy if this skill bundles one; else read from GitHub raw.
INDEX_URL = "https://raw.githubusercontent.com/BioTender-max/awesome-bio-agent-skills/main/bioskill_index_v3.csv"
df = pd.read_csv(INDEX_URL) # 1,676 rows
def search_skills(query, category=None, source=None, top=15):
"""Keyword search over name+description+category+source. Returns candidate skills."""
q = query.lower()
hay = (df["skill_name"].fillna("") + " | " +
df["description"].fillna("") + " | " +
df["category"].fillna("") + " | " +
df["source_repo"].fillna("")).str.lower()
hits = df[hay.str.contains(q, regex=False)].copy()
if category:
hits = hits[hits["category"] == category]
if source:
hits = hits[hits["source_repo"] == source]
# rank: name match first, then description match
hits["_name_hit"] = hits["skill_name"].str.lower().str.contains(q, regex=False)
hits = hits.sort_values(["_name_hit", "file_count"], ascending=[False, False])
return hits[["skill_name", "category", "source_repo", "archive_path", "description"]].head(top)
print(search_skills("variant calling").to_string(index=False))
If nothing matches, broaden the query (try a synonym), or filter by category (see the table below)
and browse that category's README section.
Once you pick a candidate's archive_path (e.g. bioskills/clair3-variants):
import urllib.request
archive_path = "bioskills/clair3-variants" # from the search result
url = f"https://raw.githubusercontent.com/BioTender-max/awesome-bio-agent-skills/main/skills/{archive_path}/SKILL.md"
skill_md = urllib.request.urlopen(url, timeout=30).read().decode()
print(skill_md) # read it, then follow its instructions
Some skills ship supporting files (scripts/, references/); list a folder via the GitHub API or
clone it (Step 3) if you need them.
If raw fetch is unavailable, or you want all supporting files, clone and copy the skill folder:
Standard (international):
git clone https://github.com/BioTender-max/awesome-bio-agent-skills.git
cp -r awesome-bio-agent-skills/skills/<archive_path>/ /path/to/your/agent/skills/
China-accelerated (ghfast.top mirror) — prepend the mirror to the GitHub URL; works for cloning
this or any source repo (https://ghfast.top/https://github.com/<owner>/<repo>.git):
git clone https://ghfast.top/https://github.com/BioTender-max/awesome-bio-agent-skills.git
cp -r awesome-bio-agent-skills/skills/<archive_path>/ /path/to/your/agent/skills/
Filter searches with the category key (left column). README section links jump to the
browsable table for that category.
Category (key) | Skills | README section | Topics |
|---|---|---|---|
genomics | 526 | Genomics | WGS/WES, variant calling, GWAS, CNV, structural variants, alignment, assembly |
biology-other | 236 | Biology and AI | Drug discovery, molecular docking, protein binder design, cheminformatics |
proteomics | 167 | Proteomics | Mass spectrometry, structure prediction (AlphaFold/ESM), binding affinity |
clinical | 152 | Clinical and Medical | EHR, clinical trials, survival analysis, ACMG, precision medicine |
single-cell | 144 | Single-Cell Analysis | scRNA-seq QC, clustering, trajectory, cell communication, spatial, multimodal |
transcriptomics | 97 | Transcriptomics | Bulk RNA-seq, differential expression, splicing, lncRNA, isoforms |
bioinformatics-general | 86 | Bioinformatics Utilities | BLAST, MSA, phylogenetics, sequence utilities, stats libraries |
multi-omics | 69 | Multi-Omics Integration | MOFA, DIABLO, integration, spatial multi-omics |
database-query | 63 | Database Query | UniProt, PDB, KEGG, Reactome, GEO, ClinVar, Ensembl, dbSNP |
visualization | 48 | Visualization | Volcano plots, heatmaps, UMAP, genome tracks, interactive charts |
workflow | 38 | Workflow Orchestration | Snakemake, Nextflow, CWL, WDL, HPC orchestration, lab automation |
epigenomics | 19 | Epigenomics | ChIP-seq, ATAC-seq, DNA methylation, Hi-C, chromatin state |
pathway | 15 | Pathway Analysis | KEGG, Reactome, GO enrichment, GSEA |
metagenomics | 9 | Metagenomics | 16S, Kraken2/Bracken, MetaPhlAn, QIIME2 |
protein-design | 7 | Protein Design | RFdiffusion, ProteinMPNN, Boltz, Chai, LigandMPNN |
Skills are aggregated and deduplicated from these repositories. Use the source_repo key to
filter the index. (bio-agent-skills-hub is this self-referential hub entry.)
Source repo (source_repo) | Skills | Focus |
|---|---|---|
bioskills — GPTomics/bioSkills | 536 | Systematic bioinformatics suite from QC to multi-omics. |
openclaw — FreedomIntelligence/OpenClaw-Medical-Skills | 359 | Medical AI library aggregating 12 specialized sub-repositories. |
sciagent — jaechang-hits/SciAgent-Skills | 154 | Statistics, databases, and clinical decision skills. |
kdense — K-Dense-AI/scientific-agent-skills | 102 | General scientific computing and HPC workflow skills. |
neuroclaw — CUHK-AIM-Group/NeuroClaw | 86 | Neuroimaging: sMRI, fMRI, dMRI, EEG (BIDS, FreeSurfer, FSL, fMRIPrep). |
clawbio — ClawBio/ClawBio | 63 | Bioinformatics workflow orchestration for GWAS and single-cell. |
labclaw — wu-yc/LabClaw | 59 | Lab automation and biomedical research skills. |
drugclaw — QSong-github/DrugClaw | 57 | Drug intelligence: DTI, ADR, DDI, pharmacogenomics, repurposing. |
nobel — ChrisLou-bioinfo/nobel-medicine-minds | 55 | Cognitive frameworks of Nobel Medicine laureates as SKILL.md. |
bioclaw_hub — zongtingwei/Bioclaw_Skills_Hub | 46 | Ten-category biological skills hub. |
bioclaw — Runchuan-BU/BioClaw | 37 | Core bioinformatics tools and database query skills. |
omics — fmschulz/omics-skills | 29 | Single-cell and spatial omics specialized skills. |
omicsclaw — TianGzlab/OmicsClaw | 28 | 6-omics domain skills: spatial, scRNA, bulk RNA, genomics, proteomics, metabolomics. |
adaptyv — adaptyvbio/protein-design-skills | 21 | Protein design toolkit: RFdiffusion, ProteinMPNN, Boltz, Chai. |
pantheon — aristoteleo/PantheonOS | 18 | Single-cell and spatial transcriptomics (Dynamo/Spateo team). |
evoskills — EvoScientist/EvoSkills | 13 | Research-lifecycle skills: ideation, planning, execution, writing, review. |
medgeclaw — xjtulyc/MedgeClaw | 7 | Biomedical research skills with dashboard, RStudio, JupyterLab integration. |
zamushwani — zamushwani2/biomedical-ai-skills | 4 | Cancer multi-omics analysis skills in R. |
bio-agent-skills-hub — BioTender-max/awesome-bio-agent-skills | 1 | Self-referential hub skill that indexes this collection. |
sragent — ArcInstitute/SRAgent | 1 | Intelligent SRA and GEO dataset retrieval. |
User: "I need a GWAS pipeline."
Agent: search_skills("gwas") → top hit bio-workflows-gwas-pipeline (genomics,
bioskills/gwas-pipeline); also gwas-pipeline (clawbio/gwas-pipeline) and plink2-gwas-analysis
(sciagent/plink2-gwas-analysis). Fetch
https://raw.githubusercontent.com/BioTender-max/awesome-bio-agent-skills/main/skills/bioskills/gwas-pipeline/SKILL.md and follow it. Browse more: https://github.com/BioTender-max/awesome-bio-agent-skills#genomics
User: "Preprocess and cluster my scRNA-seq data."
Agent: search_skills("scrna preprocessing", category="single-cell") →
scrna-preprocessing-clustering (bioclaw/scrna-preprocessing-clustering). Fetch its SKILL.md and
follow it. 144 single-cell skills total: https://github.com/BioTender-max/awesome-bio-agent-skills#single-cell-analysis
User: "Design a protein binder backbone."
Agent: search_skills("rfdiffusion") → rfdiffusion (protein-design, adaptyv/rfdiffusion).
Related: proteinmpnn (adaptyv/proteinmpnn), bindcraft (adaptyv/bindcraft). Fetch the SKILL.md
for the chosen tool. Browse: https://github.com/BioTender-max/awesome-bio-agent-skills#protein-design
User: "Call variants from long reads."
Agent: search_skills("variant calling", category="genomics") →
bio-long-read-sequencing-clair3-variants (bioskills/clair3-variants); for short reads see
bio-variant-calling-deepvariant (bioskills/deepvariant). Fetch and follow.
bioskill_index_v3.csv) is the single source of truth for what exists and where; always
resolve archive_path from it rather than guessing folder names.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
日本語の概要は準備中です。原文の説明を表示しています。
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 complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
日本語の概要は準備中です。原文の説明を表示しています。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. 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 complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
日本語の概要は準備中です。原文の説明を表示しています。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
日本語の概要は準備中です。原文の説明を表示しています。
ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
日本語の概要は準備中です。原文の説明を表示しています。
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, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
日本語の概要は準備中です。原文の説明を表示しています。