# ngs-chip-cutrun-peaks-qc

Plans and runs QC for ChIP-seq, CUT&RUN, and CUT&Tag, covering controls, peak calls, bigWigs, replicates, and differential binding.

- skill · `type=skill` · Codex, ChatGPT · 2 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-chip-cutrun-peaks-qc/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/ngs-chip-cutrun-peaks-qc

## What it does

This skill works from FASTQ or BAM inputs plus assay, target class, paired-end status, controls, genome build, blacklist, spike-in, replicate, and sample metadata. It helps route the analysis, select narrow or broad peaks, and review alignment, duplication, fragment size, FRiP, blacklist overlap, and replicate concordance.

For ChIP-seq, it can route to `nf-core/chipseq`; CUT&RUN and CUT&Tag can use `nf-core/cutandrun`. The dedicated runner produces QC summaries, an HTML dashboard, native SVG plots, browser-track handoff files, bigWigs, consensus peaks, and motif outputs when requested. Direct MACS2 use is limited to prepared BAM files with a known control and duplicate policy.

Resource manifests and readiness files are produced as well. The local-light resource check is advisory by default; use `--genome-build`, `--bundle-root <bundle>=<path>`, and `--require-resource-plan` when absent reference bundles must stop readiness.

## Install

### Codex (native platform)

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

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

### Claude Code

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

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

### Cursor

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

### GitHub Copilot

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

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

### Gemini CLI

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

### Windsurf (Devin Desktop)

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

## When to use it
- run QC across ChIP-seq, CUT&RUN, or CUT&Tag
- select narrow or broad peaks from target biology
- preserve control and spike-in pairing
- generate bigWigs and consensus peaks
- design a differential binding analysis

## Example prompts
- Run ChIP-seq QC for a transcription-factor target and assess replicate concordance.
- Process `chip_or_cutrun_samples.csv` and produce bigWigs plus consensus peaks.
- Plan differential binding for CUT&Tag with its control and spike-in metadata.

## 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: f489f8d72fb8a1aa.

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````text
---
name: ngs-chip-cutrun-peaks-qc
description: Run or plan ChIP-seq, CUT&RUN, or CUT&Tag QC, control handling, spike-in, peak calling, broad-vs-narrow target selection, replicate, bigWig, and differential binding workflows.
---

# ChIP/CUT&RUN Peaks QC

Use this skill for antibody-targeted enrichment workflows: ChIP-seq, CUT&RUN, or CUT&Tag. Use `ngs-atacseq-peaks-qc` for ATAC-seq.

## Essential Inputs

Confirm:

- assay: ChIP-seq, CUT&RUN, or CUT&Tag
- target class: transcription factor, histone mark, chromatin regulator, or custom target
- FASTQ/BAM inputs and paired-end status
- input DNA, IgG, no-antibody, or spike-in controls
- organism, genome build, blacklist, and spike-in genome if used
- biological replicates, conditions, batches, and sample metadata
- desired endpoint: QC, peaks, bigWigs, consensus peaks, or differential binding

## Route

Use `nf-core/chipseq` for ChIP-seq and `nf-core/cutandrun` for CUT&RUN/CUT&Tag when they fit the assay. Use direct MACS2 only for prepared BAMs with known control and duplicate policy.

Preflight command:

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

For compact FASTQ intake/QC, use the shared epigenomics execution package:

```bash
python plugins/ngs-analysis/scripts/run_fastq_assay_package.py \
  --lane epigenomics_peaks \
  --sample-sheet chip_or_cutrun_samples.csv \
  --execute
```

It records FASTQ-level QC and peak-calling readiness.

For local-light alignment, control-aware MACS2 peak calling, FRiP, bigWig tracks, consensus peaks, and motif-handoff artifacts, use the dedicated ChIP/CUT&RUN runner:

```bash
python plugins/ngs-analysis/scripts/run_chip_cutrun_peaks_qc.py \
  --sample-sheet chip_or_cutrun_samples.csv \
  --assay chipseq \
  --target-class tf \
  --peak-mode narrow \
  --bowtie2-index /refs/GRCh38/bowtie2/genome \
  --genome-size hs \
  --blacklist-bed /refs/GRCh38/blacklists/encode_blacklist.bed \
  --execute
```

This runner emits `qc/chip_cutrun_qc_summary.{tsv,json}`, `qc/chip_cutrun_qc_dashboard.html`, native SVG FRiP/peak and insert-size plots, browser-track handoff files under `tracks/`, and `motifs/motif_summary.tsv`. Add `--run-motifs --motif-genome <genome>` when HOMER motif enrichment should be executed instead of only planned.

It also emits `resources/resource_plan.json`, `resource_manifest.tsv`, `resource_env.sh`, and `resource_readiness.md`. The resource check is advisory by default for local-light runs; add `--genome-build`, `--bundle-root <bundle>=<path>`, and `--require-resource-plan` when missing registered reference bundles should block readiness.

For nf-core execution, use `plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline chipseq` or `--pipeline cutandrun`.

## Decision Points

- Choose narrow versus broad peak mode from target biology, not from convenience.
- Preserve control pairing and spike-in metadata through sample sheets.
````
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---
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