Bioinformatics pipeline for processing metagenomic sequencing data.

Install

mkdir -p .claude/skills/metagenomics && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11054" && unzip -o skill.zip -d .claude/skills/metagenomics && rm skill.zip

Installs to .claude/skills/metagenomics

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.

Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.
159 charsno explicit “when” trigger
Advanced

Key capabilities

  • Process metagenomic FASTQ files
  • Perform taxonomic profiling
  • Map functional pathways
  • Conduct AMR follow-up

How it works

It automates a shotgun metagenomics workflow including QC, host depletion, taxonomic profiling, and functional pathway analysis.

Inputs & outputs

You give it
Metagenomic FASTQ files
You get back
Taxonomic and functional profiles

When to use metagenomics

  • Processing metagenomic FASTQ files
  • Taxonomic profiling
  • Functional pathway mapping
  • AMR follow-up

About this skill

Metagenomics

Version Compatibility

Reference examples assume:

  • fastp 0.23+
  • kraken2 2.1+
  • bracken 2.8+
  • metaphlan 4+
  • humann 3.9+

Verify the environment first:

  • CLI: kraken2 --version, bracken -v, metaphlan --version, humann --version

Overview

Use this skill for shotgun metagenomics when the user needs:

  • QC and host depletion review
  • taxonomic abundance tables
  • functional pathway profiles
  • AMR or strain-level follow-up

When To Use This Skill

  • the data are shotgun metagenomics rather than amplicon sequencing
  • the user wants species or genus abundances, function, or resistance summaries
  • multiple samples need cohort-level comparison

Quick Route

  • host-associated samples: perform host depletion before interpretation
  • taxonomy only: kraken2 + bracken is a common pragmatic route
  • function only or plus taxonomy: add humann
  • strain claims require more evidence than top-level taxonomy calls

Progressive Disclosure

Expected Inputs

  • paired or single-end metagenomic FASTQ
  • sample metadata
  • taxonomy and optional function databases

Expected Outputs

  • results/taxonomy/bracken_species.tsv
  • results/taxonomy/bracken_genus.tsv
  • results/function/pathabundance.tsv
  • results/amr/amr_summary.tsv
  • qc/read_processing_summary.tsv

Starter Pattern

fastp \
  -i sample_R1.fastq.gz \
  -I sample_R2.fastq.gz \
  -o qc/sample.clean.R1.fastq.gz \
  -O qc/sample.clean.R2.fastq.gz \
  --html qc/sample.fastp.html \
  --json qc/sample.fastp.json

kraken2 \
  --db $KRAKEN_DB \
  --paired qc/sample.clean.R1.fastq.gz qc/sample.clean.R2.fastq.gz \
  --report results/taxonomy/sample.kraken.report \
  --output results/taxonomy/sample.kraken.out \
  --confidence 0.1

Workflow

1. Run read QC and optional host depletion

At minimum, inspect read quality, adapter content, and retained reads. For host-associated samples, remove host reads before community interpretation.

2. Profile taxonomy

Use a k-mer or marker-based profiler. Document the database and version because abundance results depend strongly on the reference.

3. Refine abundance tables

Convert raw classification to species or genus abundance tables suitable for cohort comparison.

4. Add function or AMR when requested

Run pathway or AMR profiling only after confirming taxonomic QC and read retention are reasonable.

5. Export cohort-ready outputs

Save per-sample tables and merged matrices with clear metadata joins.

Output Artifacts

results/
├── taxonomy/
│   ├── sample.kraken.report
│   ├── bracken_species.tsv
│   └── bracken_genus.tsv
├── function/
│   └── pathabundance.tsv
└── amr/
    └── amr_summary.tsv
qc/
├── read_processing_summary.tsv
└── sample.fastp.html

Quality Review

  • retained reads after QC should be reported explicitly
  • host-associated samples with large host contamination need a clear host depletion statement
  • avoid over-interpreting taxa with extremely low abundance
  • abundance comparisons should state whether values are relative abundance, counts, or normalized function estimates

Anti-Patterns

  • comparing outputs from different databases as if they were directly interchangeable
  • making strain-level claims from genus-level evidence
  • ignoring host contamination in human-associated or plant-associated samples
  • mixing taxonomy-only and pathway outputs without clarifying what each table means

Related Skills

Optional Supplements

  • scikit-bio

When not to use it

  • Amplicon sequencing

Prerequisites

FASTQ filesTaxonomy/function databases

Limitations

  • Limited to shotgun metagenomics

How it compares

It provides a standardized, cohort-ready metagenomics pipeline rather than manual analysis.

Compared to similar skills

metagenomics side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
metagenomics (this skill)04moReviewAdvanced
llm-evaluation62moNo flagsAdvanced
evaluating-llms-harness37moReviewAdvanced
qutip47moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

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