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 agentmods add skills/gptomics/bioskills/co-accessibilitynpx skills add GPTomics/bioSkills --skill co-accessibilitygit 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/co-accessibility)<a href="https://agentmods.dev/skills/gptomics/bioskills/co-accessibility"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/co-accessibility.svg" alt="Measured on agentmods" 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 | $0.00092 | $0.04582 |
| Opus 5 | $0.00046 | $0.02291 |
| Sonnet 5 | $0.00018 | $0.00916 |
| Haiku 4.5 | $0.00009 | $0.00458 |
Grade A, and why
bio-atac-seq-co-accessibility 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 5d 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-atac-seq-co-accessibility — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: Cicero 1.20+, monocle3 1.3+, ArchR 1.0.2+, SCENIC+ 1.0+, pycisTopic 1.0+, Signac 1.13+, GenomicRanges 1.54+, GenomicInteractions 1.36+, BSgenome.Hsapiens.UCSC.hg38 1.4+.
Verify before use:
- 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.
Co-accessibility (cis-Regulatory Linkage)
"Which enhancers connect to which promoters in my scATAC data?" -> Use cell-to-cell variability in joint accessibility of nearby peaks to infer cis-regulatory connections without explicit RNA expression. Output is a peak-pair graph with co-accessibility scores; thresholding produces enhancer-gene candidate pairs.
- R:
cicero::run_cicero(input_cds, genomic_coords)-> peak-pair connection scores - R:
ArchR::addCoAccessibility(proj)-> ArchR-internal Cicero wrapper - Python:
pycisTopic+SCENIC+for network-level inference combining ATAC + RNA + motifs
Co-accessibility is NOT 3D contact; it's a statistical association based on cell-to-cell co-variation. Strong co-accessibility correlates with Hi-C/Micro-C contacts (~30-50% concordance) but is not equivalent.
What Co-accessibility Captures vs What It Doesn't
| Captures | Misses |
|---|---|
| Peak pairs that vary together across cell states | 3D physical contacts that don't vary in accessibility |
| Cis-regulatory grammar within a cell type | Trans-chromosomal interactions |
| Active enhancer-promoter pairs | Constitutive structural contacts |
| Lineage-specific regulation | Developmental contacts that opened before scATAC sample |
| Distance-decay biology of enhancer-promoter | Hub enhancers that contact many distal targets |
For physical contact, use Hi-C, Micro-C, or PCHi-C. Co-accessibility is the chromatin-only proxy.
Algorithmic Taxonomy
| Tool | Method | Input | Output | Strength | Fails when |
|---|---|---|---|---|---|
| Cicero (Pliner 2018) | Graphical lasso on aggregated cell metacells | scATAC peak-cell matrix + cell trajectory | Peak-pair connection score (0-1) | Original, well-validated; integrates with Monocle3 | Slow on >50K cells; sensitive to alpha tuning |
| ArchR getCoAccessibility | Cicero-based; uses ArchR's metacell aggregation | ArchR project | Same as Cicero | Built-in to ArchR pipeline; faster on large datasets | Tied to ArchR; same biology as Cicero |
| SCENIC+ (Bravo 2023) | Multi-step: co-accessibility + motif scoring + RNA correlation | Multiome (ATAC + RNA) or paired | TF-driven enhancer-gene networks | Most comprehensive; multi-modal | Multiome data required; computationally heavy |
| LinkPeaks (Signac) | Pearson correlation of accessibility with paired gene expression | Multiome | Peak-gene linkage score | Direct enhancer-gene from RNA correlation | Multiome-only; not pure ATAC |
| GeneHancer / FANTOM5 / EpiMap | Bulk-derived enhancer-gene reference | None (database lookup) | Pre-computed enhancer-gene pairs | Comprehensive; published references | Cell-type-agnostic; may not match the biology of interest |
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.
- 5d ago First seen · 302 lines · 92 tokens per session scan A a77b394f69d4
bio-atac-seq-co-accessibility is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 20d ago), licensed MIT. It adds 92 tokens to every session and 4,582 once invoked, about $0.0005 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 2026-08-30.
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