Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-epigenomicsExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-epigenomics from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tooluniverse-epigenomics. Access via /tooluniverse-epigenomics in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
1.2K
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
1.2K
stars
Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.
When uncertain about any scientific fact, SEARCH databases first.
Methylation data, ChIP-seq peaks, ATAC-seq, multi-omics integration, genome-wide epigenomic statistics. Keywords: methylation, CpG, ChIP-seq, ATAC-seq, histone, chromatin, epigenetic.
NOT for: RNA-seq DEG, variant calling, gene enrichment, protein structure.
Identify data files, specific statistic, thresholds, genome build. Categorize by keywords.
See ANALYSIS_PROCEDURES.md for decision tree.
ENCODE tools:
ENCODE_search_rnaseq_experiments: assay_type ("total RNA-seq" default; fall back to "polyA plus RNA-seq"), biosample, limitENCODE_search_histone_experiments: target (e.g., "H3K27ac"), cell_type/tissue/biosample, limitGEO tools: GEO_search_rnaseq_datasets, GEO_search_atacseq_datasets -- both accept limit or max_results
GTEx tools:
GTEx_get_median_gene_expression: gene_symbol (NOT Ensembl ID)GTEx_query_eqtl: gene_symbol, tissue_id (case-sensitive exact, e.g., "Whole_Blood")Other: ensembl_lookup_gene (requires species='homo_sapiens'), ensembl_get_regulatory_features (NO "chr" prefix), SCREEN_get_regulatory_elements, ChIPAtlas_* (requires operation param), SRA_search_experiments (library_strategy: "ChIP-Seq"/"Bisulfite-Seq"/"ATAC-seq")
Global mean/median beta, probe variance, chromosome density, DMP counts.
See CODE_REFERENCE.md for full implementations.
| Pattern | Key Steps |
|---|---|
| Differential methylation | Filter probes → groups → t-test → FDR → threshold |
| Age-related CpG density | Correlate with age → FDR → map to chr → density ratio |
| Multi-omics missing data | Extract IDs → intersect → check NaN → complete case count |
| ChIP-seq annotation | Load peaks → annotate genes → classify regions |
| Methylation-expression | Align samples → correlate → FDR → anti-correlations |
Whole_Blood, Liver, Lung, Breast_Mammary_Tissue, Brain_Cortex, Heart_Left_Ventricle, Kidney_Cortex, Thyroid, Adipose_Subcutaneous, Muscle_Skeletal
| Grade | Criteria |
|---|---|
| Strong | padj < 0.01 AND abs(delta-beta) >= 0.2, replicated |
| Moderate | padj < 0.05 AND abs(delta-beta) >= 0.1 |
| Weak | padj < 0.05 but delta-beta < 0.1 |
| Insufficient | padj >= 0.05 or no replication |
Delta-beta >= 0.2 = strong effect. ChIP-seq: q < 0.01, FE >= 2 for confidence. ATAC-seq NFR < 150bp = active regulatory. Always apply BH FDR. Verify genome build consistency.
CODE_REFERENCE.md, TOOLS_REFERENCE.md, ANALYSIS_PROCEDURES.md, QUICK_START.md
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
tooluniverse-epigenomics fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend tooluniverse-epigenomics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: tooluniverse-epigenomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added tooluniverse-epigenomics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for tooluniverse-epigenomics matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: tooluniverse-epigenomics is focused, and the summary matches what you get after install.
Keeps context tight: tooluniverse-epigenomics is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in tooluniverse-epigenomics — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for tooluniverse-epigenomics matched our evaluation — installs cleanly and behaves as described in the markdown.
tooluniverse-epigenomics has been reliable in day-to-day use. Documentation quality is above average for community skills.
showing 1-10 of 28