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-qcgit 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-qc)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-qc"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-qc/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-qc"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-chipseq-qc.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 | $0.00125 | $0.04949 |
| Opus 5 | $0.00063 | $0.02475 |
| Sonnet 5 | $0.00025 | $0.00990 |
| Haiku 4.5 | $0.00013 | $0.00495 |
Grade A, and why
bio-chipseq-qc 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
97% identical to bio-chipseq-qc — 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 — 320 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+, phantompeakqualtools 1.2.2+, ChIPQC 1.42+, IDR 2.0.4+, samtools 1.19+, bedtools 2.31+, pysam 0.22+, pybedtools 0.9+, MACS2 2.2.9+, MACS3 3.0.4+.
Verify versions before relying on numerical thresholds — phantompeakqualtools has known R-version compatibility issues with R ≥ 4.0 (use kundajelab fork or pin to R 3.6).
ChIP-seq Quality Control
"Should I trust this ChIP-seq experiment?" -> Validate antibody, fragmentation, enrichment, replicate concordance, library complexity, and absence of hyper-ChIPable artifacts before committing to downstream peak calling and differential analysis.
- CLI:
Rscript run_spp.R -c=chip.bam -out=cc.txt(NSC/RSC),plotFingerprint -b chip.bam input.bam(enrichment shape),idr --samples rep1.np rep2.np(replicate IDR) - R: ChIPQC package (Carroll & Stark; computes the full ENCODE metric battery)
- Python: pysam + pybedtools for custom FRiP and library-complexity metrics
ChIP-seq fails for many independent reasons. The QC metrics below probe distinct failure modes — passing one metric does not rescue another. Antibody failure cannot be fixed by sequencing more.
The Antibody Problem is the Real Problem
Every downstream metric is conditional on antibody specificity. "ChIP-grade" on a vendor datasheet is marketing, not validation. Run the cascade:
| Step | What | Why |
|---|---|---|
| 1. Western blot | Expected MW + KO/KD negative | Confirms the antibody hits a band of the right size and loses signal in KO |
| 2. IP-Western | Pulls down the protein | Confirms IP recovery, not just recognition |
| 3. ChIP-qPCR | Known positive + known negative loci | First chromatin-context test; cheap |
| 4. ChIP-seq biological replicate | Two independent biological replicates | Reproducibility check |
| 5. KO/KD orthogonal | ChIP in KO/KD cells | Gold-standard: signal should drop to background |
| 6. Peptide array (histones) | Epicypher SNAP-ChIP or equivalent | Tests modification-state specificity |
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.
- 7d ago First seen · 320 lines · 125 tokens per session scan A a399fd3639e8
bio-chipseq-qc is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 4,949 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-chipseq-qc, differing in 12 lines, and is treated as a copy.
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