ChIPseq-QC
Performs ChIP-specific biological validation. It calculates metrics unique to protein-binding assays, such as Cross-correlation (NSC/RSC) and FRiP. Use this when you have filtered the BAM file and called peaks for ChIP-seq data. Do NOT use this skill for ATAC-seq data or general alignment statistics.
Best use case
ChIPseq-QC is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Performs ChIP-specific biological validation. It calculates metrics unique to protein-binding assays, such as Cross-correlation (NSC/RSC) and FRiP. Use this when you have filtered the BAM file and called peaks for ChIP-seq data. Do NOT use this skill for ATAC-seq data or general alignment statistics.
Teams using ChIPseq-QC should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/5-chipseq-qc/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ChIPseq-QC Compares
| Feature / Agent | ChIPseq-QC | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Performs ChIP-specific biological validation. It calculates metrics unique to protein-binding assays, such as Cross-correlation (NSC/RSC) and FRiP. Use this when you have filtered the BAM file and called peaks for ChIP-seq data. Do NOT use this skill for ATAC-seq data or general alignment statistics.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Comprehensive ChIP-seq QC Pipeline
## Overview
This skill performs a full ChIP-seq quality control analysis from aligned BAM files and peak files.
Main steps include:
- Refer to the **Inputs & Outputs** section to check inputs and build the output architecture. All the output file should located in `${proj_dir}` in Step 0.
- **Perform cross-correlation analysis** to calculate **NSC** and **RSC**.
- **Compute FRiP (Fraction of Reads in Peaks)** using peak files and aligned BAMs.
---
## Inputs & Outputs
### Inputs
```bash
${sample}.bam # filtered bam files
${sample}.narrowPeak # or broadPeak
```
### Outputs
```bash
all_chip_qc/
${sample}_spp.txt
${sample}_crosscorr.pdf
${sample}_frip.txt
```
----
### Step 0: Initialize Project
Call:
- `mcp__project-init-tools__project_init`
with:
- `sample`: all
- `task`: atac_qc
The tool will:
- Create`all_chip_qc` directory.
- Return the full path of the `all_chip_qc` directory, which will be used as `${proj_dir}`.
### Step 1: Calculate Cross-Correlation Metrics (NSC, RSC)
Call:
- mcp__qc-tools__run_phantompeakqualtools
with:
- `bam_file`: Path to BAM file
- `output_dir`: ${proj_dir}/
Output: `${sample}_spp.txt`, `${sample}_crosscorr.pdf`
### Step 2: Calculate the fraction of reads falling within peak regions.
Call:
- mcp__qc-tools__calculate_frip
with:
bam_file: Path to BAM file.
peak_file: Path to Peak file (BED/narrowPeak/broadPeak).
output_dir: ${proj_dir}/
Output: `${sample}_frip.txt`Related Skills
ux
This AI agent skill provides comprehensive guidance for creating professional and insightful User Experience (UX) designs, covering user research, information architecture, interaction design, visual guidance, and usability evaluation. It aims to produce actionable, user-centered solutions that avoid generic AI aesthetics.
lets-go-rss
A lightweight, full-platform RSS subscription manager that aggregates content from YouTube, Vimeo, Behance, Twitter/X, and Chinese platforms like Bilibili, Weibo, and Douyin, featuring deduplication and AI smart classification.
tech-blog
Generates comprehensive technical blog posts, offering detailed explanations of system internals, architecture, and implementation, either through source code analysis or document-driven research.
vly-money
Generate crypto payment links for supported tokens and networks, manage access to X402 payment-protected content, and provide direct access to the vly.money wallet interface.
thor-skills
An entry point and router for AI agents to manage various THOR-related cybersecurity tasks, including running scans, analyzing logs, troubleshooting, and maintenance.
whisper-transcribe
Transcribes audio and video files to text using OpenAI's Whisper CLI, enhanced with contextual grounding from local markdown files for improved accuracy.
grail-miner
This skill assists in setting up, managing, and optimizing Grail miners on Bittensor Subnet 81, handling tasks like environment configuration, R2 storage, model checkpoint management, and performance tuning.
ontopo
An AI agent skill to search for Israeli restaurants, check table availability, view menus, and retrieve booking links via the Ontopo platform, acting as an unofficial interface to its data.
astro
This skill provides essential Astro framework patterns, focusing on server-side rendering (SSR), static site generation (SSG), middleware, and TypeScript best practices. It helps AI agents implement secure authentication, manage API routes, and debug rendering behaviors within Astro projects.
chrome-debug
This skill empowers AI agents to debug web applications and inspect browser behavior using the Chrome DevTools Protocol (CDP), offering both collaborative (headful) and automated (headless) modes.
modal-deployment
Run Python code in the cloud with serverless containers, GPUs, and autoscaling using Modal. This skill enables agents to generate code for deploying ML models, running batch jobs, serving APIs, and scaling compute-intensive workloads.
advanced-skill-creator
Meta-skill that generates domain-specific skills using advanced reasoning techniques. PROACTIVELY activate for: (1) Create/build/make skills, (2) Generate expert panels for any domain, (3) Design evaluation frameworks, (4) Create research workflows, (5) Structure complex multi-step processes, (6) Instantiate templates with parameters. Triggers: "create a skill for", "build evaluation for", "design workflow for", "generate expert panel for", "how should I approach [complex task]", "create skill", "new skill for", "skill template", "generate skill"