metagenomics
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.zipInstalls 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.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
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:
fastp0.23+kraken22.1+bracken2.8+metaphlan4+humann3.9+
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 + brackenis a common pragmatic route - function only or plus taxonomy: add
humann - strain claims require more evidence than top-level taxonomy calls
Progressive Disclosure
- Read technical_reference.md for database choice, host contamination review, and functional profiling caveats.
- Read commands_and_thresholds.md for command-line patterns, thresholds, and output layout.
Expected Inputs
- paired or single-end metagenomic FASTQ
- sample metadata
- taxonomy and optional function databases
Expected Outputs
results/taxonomy/bracken_species.tsvresults/taxonomy/bracken_genus.tsvresults/function/pathabundance.tsvresults/amr/amr_summary.tsvqc/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
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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| metagenomics (this skill) | 0 | 4mo | Review | Advanced |
| llm-evaluation | 6 | 2mo | No flags | Advanced |
| evaluating-llms-harness | 3 | 7mo | Review | Advanced |
| qutip | 4 | 7mo | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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