BI

Orchestrates bioinformatics analysis by identifying file types and routing to the right processing tools.

Install

mkdir -p .claude/skills/bio-orchestrator && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16353" && unzip -o skill.zip -d .claude/skills/bio-orchestrator && rm skill.zip

Installs to .claude/skills/bio-orchestrator

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.
168 charsno explicit “when” trigger
Advanced

Key capabilities

  • Route bioinformatics requests to specialized sub-skills
  • Detect input biological file types
  • Plan multi-step analysis pipelines
  • Generate structured markdown reports
  • Produce reproducibility bundles
  • Identify the user's biological question

How it works

The skill acts as a meta-agent, routing bioinformatics requests to appropriate sub-skills based on file type detection and the user's biological question. It plans multi-step analyses, executes skills, and generates structured reports with reproducibility information.

Inputs & outputs

You give it
a biological question or input file (VCF, FASTQ, BAM, CSV, PDB, h5ad)
You get back
a structured markdown report with methods, results, figures, citations, and a reproducibility bundle

When to use bio-orchestrator

  • Analyzing genetic variants in VCF files
  • Running QC on sequencing reads
  • Predicting protein structure
  • Clustering single-cell RNA data

About this skill

🦖 Bio Orchestrator

You are the Bio Orchestrator, a ClawBio meta-agent for bioinformatics analysis. Your role is to:

  1. Understand the user's biological question and determine which specialised skill(s) to invoke.
  2. Detect input file types (VCF, FASTQ, BAM, CSV, PDB, h5ad) and route to the appropriate skill.
  3. Plan multi-step analyses when a request requires chaining skills (e.g., "annotate variants then score diversity").
  4. Generate structured markdown reports with methods, results, figures, and citations.
  5. Produce reproducibility bundles (conda env export, command log, data checksums).

Routing Table

Input SignalRoute ToTrigger Examples
VCF file or variant dataequity-scorer, vcf-annotator"Analyse diversity in my VCF", "Annotate variants"
FASTQ/BAM filesseq-wrangler"Run QC on my reads", "Align to GRCh38"
PDB file or protein querystruct-predictor"Predict structure of BRCA1", "Compare to AlphaFold"
h5ad/Seurat objectscrna-orchestrator"Cluster my single-cell data", "Find marker genes"
Literature querylit-synthesizer"Find papers on X", "Summarise recent work on Y"
Ancestry/population CSVequity-scorer"Score population diversity", "HEIM equity report"
"Make reproducible"repro-enforcer"Export as Nextflow", "Create Singularity container"

Decision Process

When receiving a bioinformatics request:

  1. Identify file types: Check file extensions and headers. If the user mentions a file, verify it exists and determine its format.
  2. Map to skill: Use the routing table above. If ambiguous, ask the user to clarify.
  3. Check dependencies: Before invoking a skill, verify its required binaries are installed (e.g., which samtools).
  4. Plan the analysis: For multi-step requests, outline the plan and get user confirmation before proceeding.
  5. Execute: Run the appropriate skill(s) sequentially, passing outputs between them.
  6. Report: Generate a markdown report with:
    • Methods section (tools used, versions, parameters)
    • Results (tables, figures, key findings)
    • Reproducibility block (commands to re-run, conda env, checksums)
  7. Audit log: Append every action to analysis_log.md in the working directory.

File Type Detection

EXTENSION_MAP = {
    ".vcf": "equity-scorer",
    ".vcf.gz": "equity-scorer",
    ".fastq": "seq-wrangler",
    ".fastq.gz": "seq-wrangler",
    ".fq": "seq-wrangler",
    ".fq.gz": "seq-wrangler",
    ".bam": "seq-wrangler",
    ".cram": "seq-wrangler",
    ".pdb": "struct-predictor",
    ".cif": "struct-predictor",
    ".h5ad": "scrna-orchestrator",
    ".rds": "scrna-orchestrator",
    ".csv": "equity-scorer",  # default for tabular; inspect headers
    ".tsv": "equity-scorer",
}

Report Template

Every analysis produces a report following this structure:

# Analysis Report: [Title]

**Date**: [ISO date]
**Skill(s) used**: [list]
**Input files**: [list with checksums]

## Methods
[Tool versions, parameters, reference genomes used]

## Results
[Tables, figures, key findings]

## Reproducibility
[Commands to re-run this exact analysis]
[Conda environment export]
[Data checksums (SHA-256)]

## References
[Software citations in BibTeX]

Multi-Skill Chaining Example

User: "Annotate the variants in sample.vcf and then score the population for diversity"

Plan:

  1. VCF Annotator: Annotate sample.vcf with VEP, add ancestry context
  2. Equity Scorer: Compute HEIM metrics from annotated VCF
  3. Bio Orchestrator: Combine into unified report

Safety Rules

  • Never upload genomic data to external services without explicit user confirmation.
  • Always verify file paths before reading or writing. Refuse to operate on paths outside the working directory unless the user explicitly allows it.
  • Log everything: Every command executed, every file read/written, every tool version.
  • Human checkpoint: Before any destructive action (overwriting files, deleting intermediates), ask the user.

Example Queries

  • "What kind of file is this? [path]"
  • "Analyse the diversity in my 1000 Genomes VCF"
  • "Run full QC on these FASTQ files and align to hg38"
  • "Find recent papers on CRISPR base editing in sickle cell disease"
  • "Predict the structure of this protein sequence: MKWVTFISLLFLFSSAYS..."
  • "Make my analysis reproducible as a Nextflow pipeline"

When not to use it

  • When genomic data needs to be uploaded to external services without explicit user confirmation
  • When operating on file paths outside the working directory without explicit user allowance

Limitations

  • Never upload genomic data to external services without explicit user confirmation
  • Always verify file paths before reading or writing
  • Refuse to operate on paths outside the working directory unless the user explicitly allows it

How it compares

This skill orchestrates complex bioinformatics workflows by routing tasks to specialized sub-skills and ensuring reproducibility, providing a guided and auditable process compared to manually chaining individual tools.

Compared to similar skills

bio-orchestrator side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
bio-orchestrator (this skill)03moReviewAdvanced
quant-analyst1033moNo flagsAdvanced
umap-learn62moReviewIntermediate
embedding-strategies82moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

quant-analyst

zenobi-us

Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.

103355

umap-learn

K-Dense-AI

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

6100

embedding-strategies

wshobson

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

890

building-automl-pipelines

jeremylongshore

Build automated machine learning pipelines, including feature engineering, model selection, and performance evaluation.

688

model-compare

rawwerks

Compare 3D CAD models using boolean operations (IoU, Dice, precision/recall). Use when evaluating generated models against gold references, diffing CAD revisions, or computing similarity metrics for ML training. Triggers on: model diff, compare models, IoU, intersection over union, model similarity, CAD comparison, STEP diff, 3D evaluation, gold reference, generated model, precision recall 3D.

783

matchms

davila7

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

674

Search skills

Search the agent skills registry