Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4Snpx agentmods add skills/pku-yuangroup/openai4s/bio-atac-seq-enhancer-gene-linkingWrote 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-atac-seq-enhancer-gene-linking)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-enhancer-gene-linking"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-enhancer-gene-linking/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-atac-seq-enhancer-gene-linking"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-enhancer-gene-linking.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 | $0.00121 | $0.04990 |
| Opus 5 | $0.00060 | $0.02495 |
| Sonnet 5 | $0.00024 | $0.00998 |
| Haiku 4.5 | $0.00012 | $0.00499 |
Grade A, and why
bio-atac-seq-enhancer-gene-linking 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 13d 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
98% identical to bio-atac-seq-enhancer-gene-linking — 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 — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ABC-Enhancer-Gene-Prediction 0.2.2+ (Engreitz lab), ENCODE-rE2G v1.0+ (EngreitzLab), Cicero 1.20+, GenomicInteractions 1.36+, FitHiChIP 9.1+, HiC-Pro 3.1+, FAN-C 0.9+, MACS3 3.0+, samtools 1.19+, bedtools 2.31+.
Verify before use:
- CLI:
<tool> --versionthen<tool> --helpto confirm flags - R:
packageVersion('<pkg>')then?function_nameto verify parameters - Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Enhancer-Gene Linking
"Which gene does this distal accessible region regulate?" -> Predict the enhancer's target gene using a model that combines accessibility activity, 3D contact frequency, and (optionally) sequence-based chromatin predictions. Output is a per-(enhancer, gene) score that can be thresholded for high-confidence calls.
- CLI: ABC pipeline (
run.neighborhoods.py,predict.pyfrom Engreitz lab) - CLI: ENCODE-rE2G (Snakemake-based; ENCODE 4 enhancer-gene standard)
- R: Cicero (ATAC-only; covered in atac-seq/co-accessibility)
- CLI: FitHiChIP / hichipper for HiChIP H3K27ac loops
- Database: EpiMap (Boix 2021), GeneHancer, FANTOM5 (pre-computed reference)
ABC and ENCODE-rE2G are the canonical predictors when Hi-C/Micro-C data is available. Cicero is the ATAC-only fallback. CRISPRi-FlowFISH (Fulco 2019) is the gold-standard experimental validation.
Algorithmic Taxonomy
| Method | Inputs | Mathematics | Strength | Fails when |
|---|---|---|---|---|
| ABC (Fulco 2019, Nasser 2021) | ATAC + H3K27ac + Hi-C/Micro-C | ABC = (Activity_E x Contact_E,G) / sum_e(Activity_e x Contact_e,G); threshold typically >= 0.02 | Mechanistically grounded; published gold-standard for human cell lines | Requires matched Hi-C / Micro-C; cell-type-specific; default contact uses average across 10 ENCODE cell types if Hi-C not available |
| ENCODE-rE2G (Gschwind 2023) | ATAC + H3K27ac + (Hi-C optional) | Logistic regression trained on CRISPRi-FlowFISH ground truth; uses ABC features + sequence features + distance | ENCODE 4 standard; pre-trained models for many cell types | Pre-trained models only available for ENCODE cell types; retraining requires CRISPRi data |
| Cicero (Pliner 2018) | scATAC peak-cell matrix | Graphical lasso on metacell co-accessibility | ATAC-only; works without Hi-C | Less concordant with Hi-C than ABC; cis-distance-limited; alpha-sensitive |
| HiChIP H3K27ac + FitHiChIP | H3K27ac HiChIP | Statistically significant loops at FDR < 0.05 | Direct experimental loop measurement; cell-type-specific; orthogonal to ATAC | Requires HiChIP wet-lab; only captures loops within HiChIP resolution (~10 kb) |
| Hi-C + HiCCUPS | Bulk Hi-C | Fold-enrichment loop calling | Most-validated 3D contact method | Resolution typically 5-25 kb; misses sub-loop fine structure |
| Capture Hi-C / PCHi-C (CHiCAGO) | Promoter Capture Hi-C | Asymptotic CHiCAGO score | High-resolution promoter-anchored | Wet-lab cost; promoter capture only |
| EpiMap (Boix 2021) reference | None (pre-computed lookup) | Bulk-derived enhancer-gene predictions in 833 epigenomes | Fast, comprehensive | Cell-type-agnostic for tissues outside the reference set |
| GeneHancer / FANTOM5 (legacy) | None (pre-computed lookup) | Pre-computed; varied methods per database | Comprehensive lookup; widely cited | Older; less reliable than ABC for cell-type-specific |
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.
- 13d ago First seen · 305 lines · 121 tokens per session scan A cf5fb75a5d71
bio-atac-seq-enhancer-gene-linking is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 4,990 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-atac-seq-enhancer-gene-linking, differing in 12 lines, and is treated as a copy.
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