# ngs-amplicon-microbiome

A skill for marker-gene microbiome analysis from amplicon FASTQs: 16S, 18S, ITS, COI. Runs nf-core/ampliseq, QIIME2 and DADA2 workflows.

- skill · `type=skill` · Codex, ChatGPT · 3 installs · ★ 7596
- Author: OpenAI · official OpenAI repository
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/openai/plugins/tree/HEAD/plugins/ngs-analysis/skills/ngs-amplicon-microbiome/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/ngs-amplicon-microbiome

## What it does

The skill takes amplicon FASTQs and carries them toward an ASV table, taxonomy, diversity or plots. It first confirms the inputs: marker region, primer sequences and orientation, paired-end or single-end reads, whether reads should be merged, taxonomy database and version, and sample metadata. Without that context it will not present a run as analysis-ready.

For reproducible end-to-end runs it prefers `nf-core/ampliseq`. QIIME2 or DADA2 directly — when you want notebook-level control or an existing lab protocol requires it. Before a real run it verifies primer trimming and truncation choices against read-quality profiles instead of picking lengths up front. Mixing taxonomy database versions without recording them makes runs hard to compare, so the skill keeps them explicit.

The local FASTQ package takes a sample sheet, validates read paths and structure, runs seqkit stats and FastQC/MultiQC when available, and writes `amplicon_analysis_status.json`. With FASTQs alone that is a read-QC and readiness bundle, not a finished analysis. A backend run needs primers, metadata and taxonomy resources; the direct DADA2 path checks for `Rscript` and the `dada2` R package before executing. When downstream tables are labelled synthetic or carry samples missing from the real sample sheet, the run is marked review-only and beta diversity is blocked unless `--allow-synthetic-diversity` is passed explicitly.

## Install

### Codex (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add openai/plugins --skill ngs-amplicon-microbiome -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/ngs-amplicon-microbiome/SKILL.md` — For all projects use ~/.agents/skills/

### Claude Code

1. **Skills CLI (Vercel)**
```bash
npx skills add openai/plugins --skill ngs-amplicon-microbiome -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/ngs-amplicon-microbiome/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/ngs-amplicon-microbiome/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/ngs-amplicon-microbiome/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install openai/plugins ngs-amplicon-microbiome
```

2. **Manually: put the files in place**
- File: `.github/skills/ngs-amplicon-microbiome/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/openai/plugins --path plugins/ngs-analysis/skills/ngs-amplicon-microbiome
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ngs-amplicon-microbiome/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/openai/plugins/tree/HEAD/plugins/ngs-analysis/skills/ngs-amplicon-microbiome/SKILL.md

## When to use it
- run a 16S or ITS amplicon analysis from FASTQs
- check read readiness before choosing primers and a denoiser
- build an ASV table and taxonomy through QIIME2 or DADA2
- compute alpha diversity and Bray-Curtis PCoA
- track which taxonomy database versions a run used

## Example prompts
- Run the amplicon microbiome preflight: `python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline amplicon_microbiome --emit-install-plan`
- Run the local package on `amplicon_samples.tsv` and show me the read readiness status
- Start the QIIME2 backend with my primers and the silva-138 classifier, metadata in `sample_metadata.tsv`

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 57011dbcfc17adf4.

BEGIN THIRD-PARTY FILE 57011dbcfc17adf4
````text
---
name: ngs-amplicon-microbiome
description: Kick off public 16S, 18S, ITS, COI, or other marker-gene amplicon microbiome workflows using nf-core/ampliseq, QIIME2, DADA2, and Cutadapt.
---

# Amplicon Microbiome

Use this skill for marker-gene microbiome analysis from amplicon FASTQs.

## Essential Inputs

Confirm:

- marker region: 16S, 18S, ITS, COI, or custom
- primer sequences and orientation
- paired-end or single-end reads
- whether reads should be merged
- taxonomy database and version
- sample metadata
- endpoint: ASV table, taxonomy, diversity, differential abundance, or plots

## Public Defaults

Prefer `nf-core/ampliseq` for reproducible end-to-end runs. Use QIIME2 or DADA2 directly when the user wants notebook-level control or an existing lab protocol requires it.

## Preflight

```bash
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline amplicon_microbiome --emit-install-plan
```

## Local Execution Package

For FASTQ intake/QC before primer, ASV, and taxonomy decisions, use:

```bash
python plugins/ngs-analysis/scripts/run_fastq_assay_package.py \
  --lane amplicon_microbiome \
  --sample-sheet amplicon_samples.tsv \
  --execute
```

This validates read paths and structure, runs seqkit stats and FastQC/MultiQC when available, and writes `amplicon_analysis_status.json`. The runner now also emits `methods/amplicon_methods.json` plus a concrete backend handoff bundle under `workflow/` so primer, denoiser, truncation, normalization, and taxonomy choices are machine-readable even before a full backend is run.

If the user asks for a full amplicon analysis rather than QC/readiness, do not treat FASTQs alone as sufficient. Require primer sequences, primer orientation, taxonomy database plus version, and sample metadata before presenting the run as analysis-ready. Without that context, run the local execution package and describe the result as a read-QC/readiness bundle only.

For backend ASV/taxonomy/diversity execution when primers, metadata, and taxonomy resources are available, use:

```bash
python plugins/ngs-analysis/scripts/run_amplicon_microbiome.py \
  --sample-sheet amplicon_samples.tsv \
  --backend qiime2 \
  --primer-forward GTGYCAGCMGCCGCGGTAA \
  --primer-reverse GGACTACNVGGGTWTCTAAT \
  --taxonomy-classifier silva-138-classifier.qza \
  --metadata sample_metadata.tsv \
  --execute
```
````
END THIRD-PARTY FILE 57011dbcfc17adf4

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