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 chip-deep-learninggit 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/chip-deep-learning)<a href="https://agentmods.dev/skills/gptomics/bioskills/chip-deep-learning"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/chip-deep-learning/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/chip-deep-learning"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/chip-deep-learning.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 | $0.00214 | $0.03830 |
| Opus 5.5 | $0.00086 | $0.01532 |
| Sonnet 5.5 | $0.00043 | $0.00766 |
| Haiku 4.5 | $0.00021 | $0.00383 |
Grade A, and why
bio-chipseq-chip-deep-learning 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 1mo 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-chip-deep-learning — 95% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: chrombpnet 0.1.7+, BPNet 0.0.23+, TF-MoDISco-lite 2.0+, EnFormer (Avsec lab Colab + DeepMind release), tensorflow 2.13+, pytorch 2.0+, JASPAR 2026 deep-learning collection (released 2025).
Deep Learning for ChIP-seq
"Predict TF binding from sequence and quantify variant effects on binding" -> Train base-resolution convolutional / transformer models on ChIP-seq / ChIP-nexus / CUT&RUN profiles; predict reference and alternate-allele binding profiles for variants; extract motif syntax via TF-MoDISco from sequence-attribution scores.
- Python (modern): chrombpnet (bias-factorized; ATAC/DNase/ChIP)
- Python (canonical TF ChIP): BPNet (originally for ChIP-nexus; soft motif syntax)
- Python (long-range): EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input window, ~100 kb effective receptive field; tissue-aggregated training)
- Python (multi-task): DeepSEA (Zhou 2015; older but still used)
- Precomputed: JASPAR 2026 Deep Learning collection (1259 BPNet ChIP models from ENCODE; 240 TFs)
Deep-learning ChIP-seq models predict signal from sequence; their power is in counterfactual variant prediction (effect on binding from a SNP) and discovery of soft motif syntax that PWMs miss (cooperativity, spacing).
Model Taxonomy
| Model | Year | Architecture | Receptive field | Best for |
|---|---|---|---|---|
| BPNet (Avsec 2021 Nat Genet 53:354) | 2021 | CNN with dilated convolutions | ~1 kb | TF ChIP-nexus / ChIP-exo; base-resolution profile prediction; soft motif syntax |
| chromBPNet (Pampari A et al 2024 bioRxiv) | 2024 | Bias-factorized CNN | ~1-2 kb | ATAC/DNase + ChIP base-resolution; bias-corrected variant effects |
| EnFormer (Avsec 2021 Nat Methods 18:1196) | 2021 | Transformer | ~100 kb effective receptive field (input window 196 kb) | Long-range regulatory predictions; cross-tissue; variant effects spanning enhancer-gene |
| DeepSEA (Zhou 2015) | 2015 | CNN multi-task | 1 kb | Predicts presence/absence across many chromatin features simultaneously |
| DeepBind (Alipanahi 2015) | 2015 | CNN binary classifier | ~50-200 bp | TF binding presence (older, less precise than BPNet) |
| Basset (Kelley 2016) | 2016 | CNN | ~600 bp | DNase / ATAC accessibility prediction |
| JASPAR 2026 Deep Learning collection | 2025 | Precomputed BPNet | ~1 kb | 1259 ENCODE TF ChIP-seq models; 240 TFs; ready-to-use |
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.
- 1mo ago First seen · 286 lines · 214 tokens per session scan A 2c5850fa281c
bio-chipseq-chip-deep-learning is a skill published in the GitHub repository GPTomics/bioSkills (1,226 stars, last pushed 2mo ago), licensed MIT. It adds 214 tokens to every session and 3,830 once invoked, about $0.0009 per session on Opus 5.5. 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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