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Metagenomics

Community
BioTender-max
metagenomics

Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.

Overview

PublisherBioTender-max
Repositoryawesome-bio-agent-skills
Skill namemetagenomics
Stars
204
Forks
36
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by BioTender-max on GitHub. Read the source before you install it.

Installation

Install the Metagenomics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/metagenomics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git /tmp/awesome-bio-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-bio-agent-skills/skills/bioclaw/metagenomics .claude/skills/metagenomics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metagenomics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Metagenomics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Metagenomics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
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

text
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

  • Microbiome Amplicon
  • Pathogen Epidemiological Genomics
  • Phylogenetics

Optional Supplements

  • scikit-bio

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Metagenomics AI skill do?

Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.

Why use Metagenomics on TypingMind?

Because you install it once and use it with any model. Metagenomics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Metagenomics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BioTender-max/awesome-bio-agent-skills/tree/main/skills/bioclaw/metagenomics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Metagenomics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Metagenomics?

As many as you like. As long as a model supports skills, you can use Metagenomics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Metagenomics AI skill free?

It is published on GitHub by BioTender-max. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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