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.
npx skills add zamushwani/biomedical-ai-skills --skill epigenomicsgit clone --depth 1 https://github.com/zamushwani/biomedical-ai-skillsWrote 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.
[](https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/epigenomics)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
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.
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.
- 2mo ago First seen · 351 lines · 0 tokens per session scan A ebf94bd22ebd
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.
epigenomics compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| epigenomicszamushwani/biomedical-ai-skills | Skill | 1 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| cerna-analysisaipoch/medical-research-skills | Skill | 2.0k | 1mo ago | MIT | A | 68 | Claude Code, Codex |
| cibersort-immune-infiltration-analysisaipoch/medical-research-skills | Skill | 2.0k | 1mo ago | MIT | A | 92 | Claude Code, Codex |
| gene-protein-expression-matrix-normalizationaipoch/medical-research-skills | Skill | 2.0k | 1mo ago | MIT | A | 63 | Claude Code, Codex |
| genomic-intelligenceK-Dense-AI/scientific-agent-skills | Skill | 56k | 5d ago | MIT | A | 151 | Claude Code, Codex |
| torch-geometricK-Dense-AI/scientific-agent-skills | Skill | 56k | 5d ago | MIT | A | 71 | Claude Code, Codex |
| bidsK-Dense-AI/scientific-agent-skills | Skill | 56k | 5d ago | MIT | A | 84 | Claude Code, Codex |