Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Browse and install community skills from the BioClaw Skills Hub. Use when a user's task is not covered by built-in skills, or when the user asks about available skills, advanced workflows, or specialized analysis pipelines. Triggers on "skills hub", "more skills", "install skill", "community skills", "find a skill for".
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Search, browse, and install community-contributed skills from the BioClaw Skills Hub.
The Hub contains 70+ specialized bioinformatics skills organized into domains. Skills downloaded from the Hub are cached locally so they persist for the rest of the session.
The Hub organizes skills into these domains:
| Domain | Examples |
|---|---|
core-bioinformatics | alignment-and-mapping, read-qc, sequence-io, database-access |
transcriptomics | bulk-rna-expression, differential-expression |
single-cell-and-spatial | scrna-preprocessing, spatial-transcriptomics, cell-annotation |
epigenomics-and-regulation | atac-seq, chip-seq, dna-methylation |
genomics-and-variation | variant-calling, genome-assembly, long-read-genomics |
metagenomics-and-microbiome | metagenomics, phylogenetics, microbial-community |
proteomics-and-metabolomics | mass-spec, metabolomics |
multi-omics-and-systems | multi-omics-integration, pathway-analysis |
protein-design | alphafold2-multimer, proteinmpnn, rfdiffusion, boltzgen |
ehr-analysis | electronic health record analysis |
curl -sL "https://raw.githubusercontent.com/zongtingwei/Bioclaw_Skills_Hub/main/catalog/taxonomy.yaml"
This returns the full skill catalog organized by domain. Use it to find the skill name that matches the user's need.
curl -sL "https://api.github.com/repos/zongtingwei/Bioclaw_Skills_Hub/contents/skills/<domain>" | python3 -c "
import json, sys
for item in json.load(sys.stdin):
if item['type'] == 'dir':
print(item['name'])
"
Replace <domain> with a domain name from the table above.
# Download the SKILL.md
DOMAIN="<domain>"
SKILL="<skill-name>"
CACHE_DIR="/workspace/group/.hub-skills/${SKILL}"
mkdir -p "${CACHE_DIR}"
curl -sL "https://raw.githubusercontent.com/zongtingwei/Bioclaw_Skills_Hub/main/skills/${DOMAIN}/${SKILL}/SKILL.md" \
-o "${CACHE_DIR}/SKILL.md"
Then read the downloaded skill:
read_file({ file_path: "/workspace/group/.hub-skills/<skill-name>/SKILL.md" })
Some Hub skills require extra Python packages. Check the SKILL.md for a "Preferred Tools" or "Dependencies" section. Install with:
pip install <package> --quiet 2>/dev/null
Follow the workflow described in the downloaded SKILL.md, just like any built-in skill.
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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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.
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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.
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Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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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.
The English summary is being prepared. Showing the original description.