Bio Agent Skills Hub

Bio Agent Skills Hub

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.

• Repository: https://github.com/BioTender-max/awesome-bio-agent-skills
• Machine-readable index (authoritative): 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.md on

> demand, and (4) follow it. Skill bodies are fetched from GitHub on demand; a git clone

> fallback (incl. a China mirror) is provided for offline / bulk / private use.

How to use this skill (router workflow)

When a user asks for any bioinformatics task, workflow, or analysis:

Step 1 — Search the index to find candidate skills

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.

Step 2 — Fetch the chosen skill's SKILL.md (content on demand)

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.

Step 3 — Offline / bulk / private fallback (clone)

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/

Categories (15) — counts and where to browse

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 |

Source repositories (20)

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. |

Example interactions

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.

Notes

• The index (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.

• Counts above (1,676 skills / 20 sources / 15 categories) match the repository README and index.
• Content fetch requires network access and a public repository; use the clone fallback otherwise.