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 spike-in-normalizationgit 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/spike-in-normalization)<a href="https://agentmods.dev/skills/gptomics/bioskills/spike-in-normalization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/spike-in-normalization/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/spike-in-normalization"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/spike-in-normalization.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 | $0.00175 | $0.04672 |
| Opus 5 | $0.00088 | $0.02336 |
| Sonnet 5 | $0.00035 | $0.00934 |
| Haiku 4.5 | $0.00017 | $0.00467 |
Grade A, and why
bio-chipseq-spike-in-normalization 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-chipseq-spike-in-normalization — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, ChIPseqSpikeInFree 1.6+, SpikChIP 1.0+, SpikeFlow (NAR Genom Bioinform 2024), samtools 1.19+, bowtie2 2.5+.
ChIP-seq Spike-In Normalization
"Account for global signal changes that defeat standard normalization" -> Add exogenous reference chromatin (Drosophila for human/mouse ChIP-Rx; E. coli carryover for CUT&RUN/CUT&Tag) at fixed concentration BEFORE IP, derive scaling factors from spike-in read counts, and apply at the read or size-factor level (never to peak counts) to enable quantitative cross-condition comparison.
- CLI: align reads to combined target + spike genome; count spike reads via
samtools view -c - R (DiffBind integration):
dba.normalize(obj, spikein = TRUE) - R (DESeq2 / edgeR):
sizeFactors(dds) <- 1 / scale_factors(note inverse) - CLI (deepTools tracks):
bamCoverage --scaleFactor <derived>(use alone;--normalizeUsingcompounds with it) - Wrapper: SpikeFlow (Snakemake; 2024) automates end-to-end
- Post-hoc detection: ChIPseqSpikeInFree (when no spike-in was added)
The fundamental rule: spike-in scaling is applied at the READ level (via size factors or --scaleFactor), never multiplied into peak counts. This is a common implementation error in published spike-in ChIP.
When Spike-In Is Required
| Experimental design | Spike-in needed? |
|---|---|
| HDAC inhibitor -> global H3K27ac increase | Yes |
| BET inhibitor (JQ1, OTX015) -> global BRD4 / H3K27ac decrease | Yes |
| EZH2 inhibitor -> global H3K27me3 loss | Yes |
| DNMT inhibitor -> global 5mC loss; downstream histone mark shifts | Yes |
| Target factor knockdown / degron | Yes (or matched-input subtraction) |
| Cell-cycle synchronization / arrest | Yes |
| Dosage titration | Yes |
| Standard TF perturbation, local rebinding expected | No (reads-in-peaks RLE works) |
| Histone mark cross-cell-type comparison | Recommended |
| CUT&RUN/CUT&Tag standard | E. coli carryover (automatic); deliberate Drosophila for high-stakes |
| Replicate-only experiment, no condition comparison | No |
What ships with it
2 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 · 349 lines · 175 tokens per session scan A 59a3f4da425f
bio-chipseq-spike-in-normalization is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 175 tokens to every session and 4,672 once invoked, about $0.0009 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 2026-09-03.
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