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 GPTomics/bioSkills --skill peak-annotationgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/peak-annotation)<a href="https://agentmods.dev/skills/gptomics/bioskills/peak-annotation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/peak-annotation/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/gptomics/bioskills/peak-annotation"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/peak-annotation.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 | $0.00179 | $0.04896 |
| Opus 5 | $0.00089 | $0.02448 |
| Sonnet 5 | $0.00036 | $0.00979 |
| Haiku 4.5 | $0.00018 | $0.00490 |
Grade A, and why
bio-chipseq-peak-annotation scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed Copies of this mod
1 near-identical copy found in the catalogue:
- bio-chipseq-peak-annotation — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ChIPseeker 1.38+, GenomicFeatures 1.54+, rtracklayer 1.62+, HOMER 4.11+, rGREAT 2.4+, chipenrich 2.26+, pyranges 0.0.129+, pandas 2.2+.
ENCODE cCRE registry expanded to 2.37M human and 967k mouse elements (Moore JE et al 2026 Nature). SCREEN web app at screen.encodeproject.org provides browser access; ENCODE provides bed files for batch annotation.
Peak Annotation
"What genes and regulatory elements do my peaks correspond to?" -> Assign each peak to a genomic feature (promoter, exon, intron, intergenic), its target gene (via nearest-TSS or host-gene), and where applicable an ENCODE cCRE class (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3).
- R (gene-feature):
ChIPseeker::annotatePeak(peaks, TxDb=txdb) - CLI (gene-feature):
annotatePeaks.pl peaks.bed hg38 -gtf annotation.gtf - Python (custom): pyranges + pandas
- R (cCRE classification): intersect peaks with ENCODE cCRE BED from SCREEN
- R (gene-set enrichment):
rGREAT::great()orchipenrich::chipenrich()
The single biggest source of misinterpretation is the nearest-TSS vs host-gene distinction (see below). For enhancer-driven biology, ENCODE-rE2G or ABC (in atac-seq/enhancer-gene-linking) is more accurate than nearest-TSS.
Choosing an Annotation Approach
| Context | Recommended | Why |
|---|---|---|
| Standard genome, pre-built annotations available | ChIPseeker with TxDb package | Simplest; automatic gene symbol mapping via annoDb |
| Custom or project-specific GTF | ChIPseeker + makeTxDbFromGFF, HOMER -gtf, or pyranges | All three handle custom annotations |
| HOMER already in pipeline | HOMER annotatePeaks.pl | Reuses tag directory; combined with motif workflow |
| Fine-grained control | pyranges (Python) | Full control over priority rules, distance calculation |
| Enhancer peaks (distal regulatory) | GREAT / rGREAT | Regulatory domain assignment (basal + extension), not just nearest |
| Cell-type-specific enhancer-gene linking | ENCODE-rE2G | Modern (2024); ABC-trained logistic regression with chromatin context |
| Gene-set enrichment with locus-length adjustment | chipenrich / Broad-Enrich | Corrects for systematic gene-length bias in peak assignment |
| Compare against ENCODE cCRE atlas | SCREEN cCRE BED intersect | Cross-reference standard regulatory registry |
| Promoter-coverage decomposition | bedtools intersect with TSS windows | Quick stats per peak set |
What ships with it
3 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.
- 6d ago First seen · 346 lines · 179 tokens per session scan A 8ccd95dd8422
bio-chipseq-peak-annotation is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 179 tokens to every session and 4,896 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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