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/differential-accessibilitynpx skills add GPTomics/bioSkills --skill differential-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/differential-accessibility)<a href="https://agentmods.dev/skills/gptomics/bioskills/differential-accessibility"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/differential-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 | $0.00094 | $0.05824 |
| Opus 5 | $0.00047 | $0.02912 |
| Sonnet 5 | $0.00019 | $0.01165 |
| Haiku 4.5 | $0.00009 | $0.00582 |
Grade A, and why
bio-atac-seq-differential-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-differential-accessibility — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.
Before using code patterns, verify installed versions match:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Differential Accessibility
"Find chromatin regions that change accessibility between my conditions" -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.
- R (consensus-peak workflow):
DiffBind-> count -> normalize -> contrast -> analyze - R (window-based, no peak set):
csaw::windowCounts+filterWindowsGlobal+ edgeR QL F-test - R (existing peak-count matrix):
DESeq2oredgeRdirectly onfeatureCountsoutput
DiffBind is a wrapper around DESeq2 / edgeR with ATAC-aware defaults. csaw is the only peak-free option; it tests fixed-width sliding windows. The choice depends on whether peaks are stable across conditions (use DiffBind) or whether some condition has dramatically different peak structure (use csaw or rebuild consensus peaks).
Algorithmic Taxonomy
| Tool | Model | Input | Min reps | Strength | Fails when |
|---|---|---|---|---|---|
| DiffBind 3.x (default DESeq2) | NB GLM via DESeq2 on consensus peaks | BAM + peak files | 2-3 per group | ATAC-aware defaults; built-in QC; blocking factors. Default in 3.x is normalize=DBA_NORM_LIB with library=DBA_LIBSIZE_FULL (full library size, background-included) |
Peaks differ dramatically between conditions (closed -> open shifts width); fewer than 2 reps per group |
| DiffBind with edgeR backend | NB GLM via edgeR-QL on consensus peaks | Same | 2-3 per group | Robust at low replicates (n=2 OK); QL test calibrates dispersion better than DESeq2 at small n | When global accessibility shifts dominate, switch to spike-in or full-library (library=DBA_LIBSIZE_FULL), never reads-in-peaks |
| DESeq2 directly on peak counts | NB GLM with shrinkage | featureCounts SAF | 3+ | Maximum control; integrates with apeglm shrinkage; modern interface | Need to manually build consensus peakset; per-region pre-filter required (low counts inflate dispersion) |
| edgeR QL F-test on peak counts | NB QL (quasi-likelihood) | featureCounts | 2 | Calibrated FDR at low n (n=2 viable); robust to outlier reps | Manual consensus peakset; small library bias unless normalization explicit |
| csaw (windows) | edgeR-QL on sliding windows | BAM only | 2 | No peak set required; detects diffuse changes peaks miss; merges adjacent windows | Computationally heavy; window size choice biases results; harder to annotate downstream |
| limma-voom | linear model with mean-variance trend | log2(CPM+offset) | 3 | Fast; good calibration at moderate count | Mis-calibrated at very low counts (atac peaks often have dropouts); needs explicit voom normalization |
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 · 350 lines · 94 tokens per session scan A c5074c087380
bio-atac-seq-differential-accessibility is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 20d ago), licensed MIT. It adds 94 tokens to every session and 5,824 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…