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 PKU-YuanGroup/OpenAI4S --skill bio-chip-seq-chipseq-visualizationgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-chip-seq-chipseq-visualization)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-visualization"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-visualization/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/pku-yuangroup/openai4s/bio-chip-seq-chipseq-visualization"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-visualization.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 | $0.00158 | $0.03866 |
| Opus 5 | $0.00079 | $0.01933 |
| Sonnet 5 | $0.00032 | $0.00773 |
| Haiku 4.5 | $0.00016 | $0.00387 |
Grade A, and why
bio-chipseq-visualization 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 7d 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.
This is a copy
95% identical to bio-chipseq-visualization — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: deepTools 3.5+, pyGenomeTracks 3.9+, Gviz 1.46+, EnrichedHeatmap 1.32+, ChIPseeker 1.38+, IGV 2.17+, samtools 1.19+, bedtools 2.31+.
ChIP-seq Visualization
"Visualize ChIP-seq signal around features of interest" -> Generate normalized signal tracks (bigWig), heatmaps centered on TSS/peaks, average profile plots, and genome-browser views — with normalization that supports the biological claim (within-sample vs cross-sample vs spike-in scaled).
- CLI (production): deepTools
bamCoverage->computeMatrix->plotHeatmap/plotProfile - CLI (config-driven tracks): pyGenomeTracks (replaces Gviz for many use cases)
- R (publication): Gviz, EnrichedHeatmap, ChIPseeker tag heatmaps
- GUI: IGV with batch scripts for reproducible screenshots
The single most consequential choice is bigWig normalization — it determines whether visual comparison reflects biology. Get this right before generating any heatmap or browser view.
bigWig Normalization Decision Tree
| Goal | Method | When to use |
|---|---|---|
| Within-sample profile of a single ChIP | --normalizeUsing CPM |
Standard; reads per million; comparable within one library |
| Within-sample, length-aware | --normalizeUsing BPM |
TPM-analog; useful for variable-width regions; less common for ChIP-seq |
| Cross-sample with equal effective depth | --normalizeUsing RPGC --effectiveGenomeSize <N> |
"1x genome coverage" — assumes equal sequencing genome-wide; ENCODE convention |
| Cross-condition with global signal change | --scaleFactor <spike_in_derived> (skip --normalizeUsing) |
HDACi / BETi / EZH2i; see chip-seq/spike-in-normalization |
| ChIP vs input ratio | bamCompare --operation log2 |
Visualize enrichment over input |
| ChIP vs input control-subtracted | bamCompare --operation subtract |
Absolute signal above background |
| ChIP vs input SES-corrected | bamCompare --scaleFactorsMethod SES --operation log2 |
More robust to library size; uses signal-extraction-scaling |
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.
- 7d ago First seen · 354 lines · 158 tokens per session scan A 8f92b756a38a
bio-chipseq-visualization is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (399 stars, last pushed yesterday), licensed MIT. It adds 158 tokens to every session and 3,866 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-chipseq-visualization, differing in 12 lines, and is treated as a copy.
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