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 TianGzlab/OmicsClaw --skill chip-atlas-peak-enrichmentgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/chip-atlas-peak-enrichment)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/chip-atlas-peak-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/chip-atlas-peak-enrichment/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/tiangzlab/omicsclaw/chip-atlas-peak-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/chip-atlas-peak-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00007 | $0.04239 |
| Opus 5 | $0.00003 | $0.02119 |
| Sonnet 5 | $0.00001 | $0.00848 |
| Haiku 4.5 | $0.00001 | $0.00424 |
Grade A, and why
ChIP-Atlas Peak Enrichment 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- chip-atlas-peak-enrichment — 92% identical, 575 lines differ
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ChIP-Atlas Peak Enrichment
Find ChIP-seq peak enrichment near your genes using the official ChIP-Atlas Enrichment Analysis API.
When to Use This Skill
Use ChIP-Atlas peak enrichment when you need to:
- Identify transcription factors binding near your genes from DE analysis or pathway results
- Discover chromatin regulators (TFs, histone modifications, chromatin remodelers) enriched near your gene set
- Validate regulatory relationships between factors and target genes using public ChIP-seq data
- Find cell-type-specific regulators by filtering to specific cell classes
- Query 433,000+ ChIP-seq experiments via the official API without manual downloads
Don't use for:
- Direct ChIP-seq analysis from raw reads (use peak calling workflows)
- Single gene lookups (use ChIP-Atlas web interface directly)
- Offline analysis (requires internet for API calls)
Key Concept: Submits your gene list to the ChIP-Atlas API, which performs Fisher's exact test enrichment analysis against all public ChIP-seq experiments. Returns fold enrichment, P-values, and BH-corrected Q-values.
Installation
| Software | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| pandas | >=1.3 | BSD-3-Clause | Permitted | pip install pandas |
| requests | >=2.25 | Apache-2.0 | Permitted | pip install requests |
| numpy | >=1.20 | BSD-3-Clause | Permitted | pip install numpy |
| plotnine | >=0.10 | MIT | Permitted | pip install plotnine |
| plotnine-prism | >=0.3 | MIT | Permitted | pip install plotnine-prism |
pip install pandas requests numpy plotnine plotnine-prism
System requirements: Internet connection (API calls to ChIP-Atlas and Ensembl)
Inputs
Gene list:
- Gene symbols (e.g., ["TP53", "MYC", "EGFR"])
- Minimum: 3 genes; Recommended: 5-100 genes
- Formats: Python list, plain text (one per line), CSV with gene column
Parameters:
- Genome: hg38 (default), hg19, mm10, mm9, rn6, dm6, dm3, ce11, ce10, sacCer3
- Antigen class: "TFs and others" (default), "Histone", "ATAC-Seq", "DNase-seq", "RNA polymerase"
- Cell class: "All cell types" (default), "Blood", "Neural", "Breast", etc.
- Threshold: Peak-calling stringency (MACS2 -10×log10(p)): 50 (default, ~p<1e-5), 100 (~p<1e-10), 200 (~p<1e-20), 500 (~p<1e-50). Higher = fewer, more confident peaks. See references/peak_thresholds.md.
- TSS window: 5000bp upstream, 5000bp downstream (default)
What ships with it
13 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.
- references/chipatlas_metadata_format.md 3.6 KB
- references/enrichment_statistics.md 4.7 KB
- references/peak_thresholds.md 2.2 KB
- references/promoter_definitions.md 2.0 KB
- scripts/__init__.py 46 B runs code
- scripts/convert_genes_to_regions.py 8.1 KB runs code
- scripts/export_all.py 24 KB runs code
- scripts/filter_experiments.py 3.8 KB runs code
- scripts/generate_all_plots.py 9.6 KB runs code
- scripts/load_example_data.py 3.4 KB runs code
- scripts/load_user_data.py 5.1 KB runs code
- scripts/query_chipatlas_api.py 10.0 KB runs code
- scripts/run_enrichment_workflow.py 9.7 KB runs code
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.
- 12d ago First seen · 218 lines · 7 tokens per session scan A cdaf0bb200bd
ChIP-Atlas Peak Enrichment is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 4,239 once invoked, about $0.0000 per session on Opus 5. 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 2026-08-30.
Other skills, from other repositories
bio-agent-skills-hub
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
single-cell-scrna-seq-analysis-scanpy
Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…
single-cell
Single-cell analysis pipeline covering scRNA-seq, snRNA-seq, and CyTOF (mass cytometry) — QC, normalization, integration, clustering, annotation, differential expression, trajectory, cell communication, and TF activity inference.