epigenomics

epigenomics is a skill for Claude Code, Codex from zamushwani/biomedical-ai-skills. It costs 0 tokens per session (4,108 once invoked), scanned A, original, MIT.

A workflow for studying chromatin, the material that packages DNA, using ATAC-seq and ChIP-seq sequencing data. It identifies accessible DNA regions or protein-binding regions and relates them to genes and transcription factors.

In plain words
What is it for?
Use it to call peaks, measure differential accessibility or binding, find enriched DNA motifs, estimate transcription-factor activity, link peaks to genes, combine chromatin data with expression, and check quality metrics.
Why use it?
It catches analysis choices that can change results, such as filtering, peak-calling settings, sample definitions, and genome annotations. It also avoids assuming that a nearby gene is necessarily regulated by a peak.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to call peaks, measure differential accessibility or binding, find enriched DNA motifs, estimate transcription-factor activity, link peaks to genes, combine chromatin data with expression, and check quality metrics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zamushwani/biomedical-ai-skills/epigenomics
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add zamushwani/biomedical-ai-skills --skill epigenomics
Clone the repo
git clone --depth 1 https://github.com/zamushwani/biomedical-ai-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for epigenomics

README.md
[![agentmods](https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/epigenomics/github.svg)](https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/epigenomics)
Your own site
<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/epigenomics"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/epigenomics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for epigenomics

Your own site · 80×15
<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/epigenomics"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/epigenomics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,108 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.04108
Opus 5.5 $0.00000 $0.01643
Sonnet 5.5 $0.00000 $0.00822
Haiku 4.5 $0.00000 $0.00411

Measured 2mo ago against content hash ebf94bd22ebd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

epigenomics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (tests/run_all.py, tests/validate_claims.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/epigenomics/SKILL.md · 351 lines

How it starts

The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Epigenomics

ATAC-seq and ChIP-seq analysis for chromatin accessibility and transcription factor binding. Covers the filtering that precedes peak calling, ATAC-specific peak calling, differential binding and the DiffBind 3.x changes that silently alter results, motif enrichment, TF activity from accessibility, and assigning peaks to genes without pretending distance is regulation.

When to Use This Skill

Activate when the user requests:

  • ATAC-seq or ChIP-seq peak calling, or MACS
  • Differential accessibility or differential binding
  • Motif enrichment, TF footprinting, or chromVAR
  • Assigning peaks to genes, or enhancer-promoter linking
  • Integrating accessibility or binding with expression
  • Chromatin QC: FRiP, TSS enrichment, fragment size distribution

Inputs

Data Type Form Note
Aligned reads BAM, duplicates marked ATAC needs chrM and blacklist removal first
Peaks narrowPeak / broadPeak narrow for TF and ATAC, broad for most histone marks
Sample sheet CSV with condition, replicate, bamReads, peaks DiffBind's entry point
Genome annotation TxDb or GTF for TSS enrichment and peak annotation
Motif database JASPAR version changes the answer

Environment

Versions verified 2026-08.

pip install MACS3          # 3.0.4  peak calling
pip install deeptools      # 3.5.6  coverage, matrices, QC plots
BiocManager::install(c("DiffBind",     # 3.22.2  differential binding
                       "csaw",         # 1.46.0  window-based differential
                       "chromVAR",     # 1.34.1  TF activity from accessibility
                       "ChIPseeker",   # 1.48.0  peak annotation
                       "motifmatchr",  # 1.34.0  motif scanning
                       "TFBSTools",    # 1.50.0
                       "JASPAR2024",   # motif database
                       "rGREAT"))      # 2.14.0  regulatory region enrichment
Use MACS3 (3.0.4, released 2026-02), not MACS2. MACS2's last release was
2023-07 and development moved to MACS3, which is actively maintained. Most
tutorials still say macs2; the command is macs3 and the options differ in
places.

JASPAR2024 is the latest Bioconductor release; JASPAR2026 does not exist.
Record which JASPAR version you used, because motif matrices are revised
between releases and enrichment results move with them.

Read the full file on GitHub · 351 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 351 lines · 0 tokens per session scan A ebf94bd22ebd

Subscribe to this mod's changes

epigenomics is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,108 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

epigenomics compared with skills from other repositories

epigenomics next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
epigenomicszamushwani/biomedical-ai-skillsSkill11mo agoMITA0Claude Code, Codex
cerna-analysisaipoch/medical-research-skillsSkill2.0k1mo agoMITA68Claude Code, Codex
cibersort-immune-infiltration-analysisaipoch/medical-research-skillsSkill2.0k1mo agoMITA92Claude Code, Codex
gene-protein-expression-matrix-normalizationaipoch/medical-research-skillsSkill2.0k1mo agoMITA63Claude Code, Codex
genomic-intelligenceK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA151Claude Code, Codex
torch-geometricK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA71Claude Code, Codex
bidsK-Dense-AI/scientific-agent-skillsSkill56k5d agoMITA84Claude Code, Codex