# ngs-fastq-qc

Checks FASTQ files, runs FastQC and MultiQC, interprets quality signals, and can create trimmed reads while keeping raw inputs intact.

- skill · `type=skill` · Codex, ChatGPT · 3 installs · ★ 7604
- 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-fastq-qc/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/ngs-fastq-qc

## What it does

This skill validates FASTQ paths, R1/R2 pairing, and run settings before local quality control. It can use FastQC and MultiQC for reports, then choose a trimming branch with `fastp` or `Cutadapt`. Explicit adapter or primer sequences are passed to `Cutadapt` when automatic handling is not appropriate.

The local runner is `run_fastq_qc.py`; it validates the setup before Snakemake execution. A run directory contains `run_manifest.json`, `config.json`, validation data, logs, `artifact_index.json`, `summary.md`, FastQC/MultiQC results, and `qc_interpretation.json`. The interpretation covers end-of-read quality loss, duplication, poly-G artifacts, contamination signals, and pairing problems.

Raw FASTQ files are never overwritten, and the original QC reports remain available when trimmed FASTQs are produced. Required tools depend on the selected branch and can include `FastQC`, `MultiQC`, `fastp`, `Cutadapt`, and `seqkit`.

## Install

### Codex (native platform)

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

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

### Claude Code

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

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

### Cursor

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install openai/plugins ngs-fastq-qc
```

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

### Gemini CLI

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

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/ngs-fastq-qc/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-fastq-qc/SKILL.md

## When to use it
- validate R1/R2 pairing before downstream analysis
- generate FastQC and MultiQC reports
- interpret quality loss at read ends
- trim adapters or primers with Cutadapt
- produce trimmed FASTQs without overwriting raw reads

## Example prompts
- Run FASTQ QC for `samplesheet.csv` and summarize the findings from `qc_interpretation.json`.
- Check `sampleA_R1.fastq.gz` and `sampleA_R2.fastq.gz` with the local `run_fastq_qc.py` runner.
- Use the `Cutadapt` branch for `samplesheet.csv` with adapter `AGATCGGAAGAGC`, keeping the raw FASTQs unchanged.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE e3aaa51085445513
````text
---
name: ngs-fastq-qc
description: Validate FASTQ inputs, run local FastQC/MultiQC QC, interpret QC signals, and optionally execute fastp or Cutadapt trimming branches without overwriting raw reads.
---

# FASTQ QC

Use this skill for QC-only, trimming-first, or FASTQ quality interpretation workflows. This skill can execute the plugin-owned local FastQ QC runner when the user approves a local run. It should decide whether trimming or additional investigation is warranted; it should not blindly trim by default.

## Essential Inputs

Confirm:

- FASTQ paths and pairing convention
- whether output should be QC-only or trimmed FASTQs
- known adapter or primer sequences
- organism if contamination screening or host depletion is requested
- output directory
- whether FASTQs are raw, demultiplexed, previously trimmed, or downloaded from an archive
- whether downstream analysis expects original read lengths, UMIs, or inline barcodes

## Public Tools

Default tool set:

- `FastQC` for raw read QC
- `MultiQC` for project-level summary
- `fastp` for all-in-one QC/trimming when acceptable
- `Cutadapt` when primer/adapter handling needs explicit sequences
- `seqkit` for quick counts, stats, and subsampling

## Preflight

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

## Local Execution

Use the plugin-owned runner for local artifact-producing FASTQ QC:

```bash
python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --execute
```

Single paired sample:

```bash
python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample sampleA \
  --r1 sampleA_R1.fastq.gz \
  --r2 sampleA_R2.fastq.gz \
  --execute
```

Optional trimming branch:

```bash
python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --trim-mode fastp \
  --execute
```

For explicit adapters:

```bash
python plugins/ngs-analysis/scripts/run_fastq_qc.py \
  --sample-sheet samplesheet.csv \
  --trim-mode cutadapt \
  --adapter-r1 AGATCGGAAGAGC \
  --adapter-r2 AGATCGGAAGAGC \
  --execute
```

The runner performs pre-execution validation before Snakemake execution. It writes a timestamped run directory with `run_manifest.json`, `config.json`, `validation/`, `workflow/Snakefile`, logs, `artifact_index.json`, `summary.md`, FastQC/MultiQC outputs, and `qc_interpretation.json` after successful execution.

## Interpretation Rules

Inspect raw QC before recommending trimming:

- Per-base quality drop at the read end: consider quality trimming, but preserve enough length for alignment or amplicon merging.
- Adapter or primer signal: use `cutadapt` when explicit sequences matter; use `fastp` only when automatic handling is acceptable.
- Poly-G or patterned-flowcell artifacts: handle with a tool that explicitly supports the artifact and report the assumption.
- Overrepresented sequences: classify adapters, primers, rRNA, PhiX, host contamination, or true biology before filtering.
- Per-tile failures or severe quality shifts: flag possible run-level issues and avoid treating them as ordinary adapter contamination.
````
END THIRD-PARTY FILE e3aaa51085445513

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