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 inflexa-ai/inflexa --skill chromatin-regulationgit clone --depth 1 https://github.com/inflexa-ai/inflexaWrote 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/inflexa-ai/inflexa/chromatin-regulation)<a href="https://agentmods.dev/skills/inflexa-ai/inflexa/chromatin-regulation"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/chromatin-regulation/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/inflexa-ai/inflexa/chromatin-regulation"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/chromatin-regulation.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 | $0.00032 | $0.01970 |
| Opus 5 | $0.00016 | $0.00985 |
| Sonnet 5 | $0.00006 | $0.00394 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
chromatin-regulation 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 2d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chromatin Regulation Analysis
Guidelines for analysing chromatin accessibility (ATAC-seq) and histone/TF binding (ChIP-seq, CUT&Tag/CUT&Run) from called peaks, count matrices, and signal tracks: differential binding, signal summarisation, peak annotation, and single-cell chromatin profiling.
Where This Pack Starts
Your input is a peak set, a count matrix, a bigWig track, or an aligned BAM you read signal from. Alignment, filtering and peak calling are upstream and out of scope — no aligner or peak caller is installed. If you are handed FASTQ or unpeaked alignments, say what processed form you need and stop.
Knowing what produced the input still matters for interpreting it. Peaks normally come from MACS2 (ATAC and ChIP) or SEACR (CUT&Tag/CUT&Run); alignments from Bowtie2 or BWA-MEM. Use those names to read the file headers and provenance you are given, not as steps to run.
What to establish about a supplied dataset, because each changes how the counts may be interpreted and none can be recovered here:
- Tn5 offset correction (ATAC): a +4/-5 bp shift. If it was not applied, base-resolution positional claims are shifted by ~4-5 bp. Peak-level differential analysis is unaffected.
- Duplicate handling: CUT&Tag produces natural duplicates by tagmentation, so removing them discards real signal. If duplicates were marked on CUT&Tag data, say so — the counts understate the signal.
- Spike-in normalisation (CUT&Tag/CUT&Run): required when comparing conditions with globally different signal levels. If the counts were not spike-in normalised, a global shift cannot be distinguished from no change, and you cannot recover the scale factor from the counts alone.
- Peak-set provenance: peak sets from different callers or settings are not comparable by width or count. Harmonise to a consensus set first.
Method-Selection Decision Tree
1. Differential binding / accessibility
Peak calls from replicated conditions
→ DiffBind (R via rpy2, works for all assay types):
→ dba() → dba.count() → dba.normalize() → dba.contrast() → dba.analyze()
→ Backend: DESeq2 (default, better for small sample sizes) or edgeR
→ dba.report() for differential peaks
→ Filter: padj < 0.05 AND abs(log2FC) > 1
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.
- 2d ago First seen · 160 lines · 32 tokens per session scan A 24a6f0c64ec3
chromatin-regulation is a skill published in the GitHub repository inflexa-ai/inflexa (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 1,970 once invoked, about $0.0002 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 2026-09-09.
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Load when summarising a peak file (BED / narrowPeak) from ATAC-seq / ChIP-seq / CUT&Tag — peak count, width distribution, per-chromosome counts, score statistics. Skip when calling peaks from BAM (run MACS / Genrich externally first); working with single-cell ATAC (use scatac-preprocessing).
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