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 super-enhancersgit 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/super-enhancers)<a href="https://agentmods.dev/skills/gptomics/bioskills/super-enhancers"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/super-enhancers/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/super-enhancers"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/super-enhancers.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 | $0.00146 | $0.04360 |
| Opus 5 | $0.00073 | $0.02180 |
| Sonnet 5 | $0.00029 | $0.00872 |
| Haiku 4.5 | $0.00015 | $0.00436 |
Grade A, and why
bio-chipseq-super-enhancers scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed Copies of this mod
1 near-identical copy found in the catalogue:
- bio-chipseq-super-enhancers — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: ROSE (stjude/ROSE, 2018+), ROSE2 (linlabbcm/rose2, 2021+), LILY (BoevaLab/LILY, 2020+), HOMER 4.11+, samtools 1.19+, bedtools 2.31+, GenomicRanges 1.54+.
The original Young-lab ROSE is Python 2; ROSE2 (linlabbcm/rose2) and the stjude/ROSE fork are the Python-3 implementations with the same algorithm. For hg38 data use stjude/ROSE (python ROSE_main.py, whose genomeDict includes HG38); rose2's released genomeDict covers only HG18/HG19/MM8/MM9/MM10/RN4/RN6, so rose2 -g HG38 fails. LILY (Boeva 2017) is a refactored implementation with input-control background subtraction for low-quality H3K27ac data.
Super-Enhancer Calling
"Identify super-enhancers driving cell identity / cancer biology" -> Stitch nearby active enhancer peaks (H3K27ac, MED1, or BRD4) within a stitching window, exclude proximal-promoter signal, rank by total signal, find the hockey-stick inflection point where signal sharply increases, and classify all stitched regions above the inflection as super-enhancers.
- CLI (stjude/ROSE, Python 3):
python ROSE_main.py -g HG38 -i peaks.gff -r h3k27ac.bam -c input.bam -s 12500 -t 2500 -o rose_out/ - CLI (HOMER):
findPeaks tag_dir/ -style super -i input_tag_dir/ - CLI (LILY): variant with input-control background subtraction
- R (custom hockey-stick): rank enhancers by signal, find tangent-line inflection
The SE concept (Whyte 2013) is a thresholding heuristic on a continuous signal distribution (Pott & Lieb 2015 Nat Genet), not a categorical biological category. Genetic dissection of super-enhancers (Hay 2016; Moorthy 2017) shows constituent elements contribute unequally and many are individually dispensable/redundant; the "SE" label is a useful operational definition for BET-inhibitor responsiveness and cell-identity gene regulation, not an absolute biological property.
Marker Choice: H3K27ac vs MED1 vs BRD4
| Marker | Captures | When to prefer |
|---|---|---|
| H3K27ac | Active regulatory elements broadly | Most widely available; standard for SE definition since Whyte 2013 |
| MED1 | Mediator complex accumulation (the defining biology) | Direct readout of SE; less common antibody; lower signal-to-noise |
| BRD4 | BET cofactor accumulation | Most predictive of BET-inhibitor responsiveness; clinical relevance |
| H3K27ac + MED1 intersection | High-confidence SE | Gold standard if both available |
| dELS from ENCODE cCREs | Cell-type-agnostic distal enhancer registry | Cross-reference; not SE-specific by itself |
What ships with it
3 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.
- 7d ago First seen · 264 lines · 146 tokens per session scan A c71ac76ba172
bio-chipseq-super-enhancers is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 26d ago), licensed MIT. It adds 146 tokens to every session and 4,360 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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…
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.
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…
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…