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 agentmods add skills/tiangzlab/omicsclaw/upstream-regulator-analysisnpx skills add TianGzlab/OmicsClaw --skill upstream-regulator-analysisgit 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/upstream-regulator-analysis)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/upstream-regulator-analysis"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/upstream-regulator-analysis.svg" alt="Measured on agentmods" 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 | $0.00005 | $0.02881 |
| Opus 5 | $0.00003 | $0.01440 |
| Sonnet 5 | $0.00001 | $0.00576 |
| Haiku 4.5 | $0.00001 | $0.00288 |
Grade A, and why
Upstream Regulator Analysis 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 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.
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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Upstream Regulator Analysis
Identify transcription factors (TFs) driving observed differential expression by integrating ChIP-Atlas TF binding data (epigenomics) with RNA-seq DE results (transcriptomics). Ranks TFs by a combined regulatory score incorporating binding enrichment, target-DE overlap (Fisher's exact test), and directional concordance (activator vs repressor).
When to Use This Skill
Use when you:
- Have DE results and want to identify TFs driving expression changes
- Need to go beyond simple gene list enrichment to mechanistic TF-level evidence
- Want to distinguish activators (targets upregulated) from repressors (targets downregulated)
- Want to integrate epigenomics (ChIP-seq) with transcriptomics (RNA-seq) in one analysis
Don't use for:
- Single-cell DE results (designed for bulk RNA-seq DE)
- Organisms not in ChIP-Atlas (see supported genomes below)
- Histone mark analysis (use
chip-atlas-peak-enrichmentdirectly) - When you only need TF binding enrichment without target gene integration
Requires: Internet access (ChIP-Atlas API + data server). Runtime: 15-25 minutes (API polling + target gene downloads).
Installation
pip install pandas numpy scipy requests matplotlib seaborn reportlab
| Package | Version | License | Commercial Use |
|---|---|---|---|
| pandas | ≥1.5 | BSD-3 | ✅ Permitted |
| numpy | ≥1.21 | BSD-3 | ✅ Permitted |
| scipy | ≥1.9 | BSD-3 | ✅ Permitted |
| requests | ≥2.28 | Apache-2.0 | ✅ Permitted |
| matplotlib | ≥3.6 | PSF | ✅ Permitted |
| seaborn | ≥0.12 | BSD-3 | ✅ Permitted |
| reportlab | ≥3.6 | BSD | ✅ Permitted |
Sibling skill dependencies: Requires chip-atlas-peak-enrichment and chip-atlas-target-genes directories at the same level.
Inputs
- DE results CSV/TSV with columns: gene symbol, log2 fold change, adjusted p-value
- Supports DESeq2 (
log2FoldChange,padj), edgeR (logFC,FDR), limma (logFC,adj.P.Val) - Column names auto-detected; override with parameters if needed
- Supports DESeq2 (
- Genome: hg38, hg19, mm10, mm9, rn6, dm6, dm3, ce11, ce10, sacCer3
What ships with it
9 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/integration_methods.md 5.4 KB
- scripts/__init__.py 47 B runs code
- scripts/export_all.py 11 KB runs code
- scripts/generate_all_plots.py 8.3 KB runs code
- scripts/generate_report.py 14 KB runs code
- scripts/load_de_results.py 5.2 KB runs code
- scripts/load_example_data.py 11 KB runs code
- scripts/run_integration_workflow.py 12 KB runs code
- scripts/score_regulons.py 5.3 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.
- 6d ago First seen · 228 lines · 5 tokens per session scan A cb1c6b1a8ab5
Upstream Regulator Analysis is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 5 tokens to every session and 2,881 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.
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