Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-expression-data-retrievalExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-expression-data-retrieval 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-expression-data-retrieval. Access via /tooluniverse-expression-data-retrieval in your agent's command palette.
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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
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Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.
IMPORTANT: Always use English terms in tool calls. Respond in the user's language.
LOOK UP DON'T GUESS: Never assume which datasets exist or their accessions. Always search to confirm.
Before retrieving, determine: organism, tissue, experimental design (case-control/time-series/dose-response). These affect which database to search and how to interpret results. RNA-seq provides wider dynamic range; microarray has extensive legacy data. Prioritize experiments with >=3 biological replicates, complete annotations, and both raw+processed data.
Phase 0: Clarify (if ambiguous) → Phase 1: Disambiguate → Phase 2: Search & Retrieve → Phase 3: Report
Ask ONLY if: gene name ambiguous, tissue/condition unclear, organism not specified. Skip for: specific accessions (E-MTAB-, E-GEOD-, S-BSST*), clear disease/tissue+organism, explicit platform requests.
Resolve official gene symbol (HGNC for human, MGI for mouse). Note common aliases for search expansion.
| User Query Type | Search Strategy |
|---|---|
| Specific accession | Direct retrieval |
| Gene + condition | "[gene] [condition]" + species filter |
| Disease only | "[disease]" + species filter |
| Technology-specific | Add platform keywords |
Search silently. Do NOT narrate the process.
# ArrayExpress search
result = tu.tools.arrayexpress_search_experiments(keywords="[gene/disease]", species="[species]", limit=20)
# Get experiment details, samples, files
details = tu.tools.arrayexpress_get_experiment(accession=accession)
samples = tu.tools.arrayexpress_get_experiment_samples(accession=accession)
files = tu.tools.arrayexpress_get_experiment_files(accession=accession)
# BioStudies for multi-omics
biostudies = tu.tools.biostudies_search(query="[keywords]", limit=10)
study = tu.tools.biostudies_get_study(accession=study_accession)
study_files = tu.tools.biostudies_get_study_files(accession=study_accession)
| Primary | Fallback |
|---|---|
| ArrayExpress search | BioStudies search |
| arrayexpress_get_experiment | biostudies_get_study |
| arrayexpress_get_experiment_files | Note "Files unavailable" |
Present as a Dataset Search Report. Hide search process. Include:
| Tier | Symbol | Criteria |
|---|---|---|
| High | ●●● | >=3 bio replicates, complete metadata, processed data available |
| Medium | ●●○ | 2-3 replicates OR some metadata gaps |
| Low | ●○○ | No replicates, sparse metadata, or access issues |
| Caution | ○○○ | Single sample, no replication, outdated platform |
Dataset quality: Prioritize >=3 biological replicates, complete annotations, both raw+processed data. Single-replicate experiments can inform but not be sole evidence.
Platform comparison: RNA-seq = wider dynamic range, novel transcripts. Microarray = probe-limited but extensive legacy data. Cross-platform combining requires batch correction.
Metadata scoring: Rate 0-5 on: (1) sample annotations, (2) design documented, (3) pipeline described, (4) raw data deposited, (5) publication linked. Score <=2 warrants caution.
GEO vs ArrayExpress: GEO has broader coverage (older studies); ArrayExpress enforces stricter metadata. BioStudies captures multi-omics. Search both.
| Error | Response |
|---|---|
| "No experiments found" | Broaden keywords, remove species filter, try synonyms |
| "Accession not found" | Verify format, check if withdrawn |
| "Files not available" | Note: "Data files restricted by submitter" |
| "API timeout" | Retry once, note "(metadata retrieval incomplete)" |
ArrayExpress: arrayexpress_search_experiments (search), arrayexpress_get_experiment (metadata), arrayexpress_get_experiment_files (downloads), arrayexpress_get_experiment_samples (annotations)
BioStudies: biostudies_search (search), biostudies_get_study (metadata+sections), biostudies_get_study_files (files)
Additional Sources:
GEO_search_rnaseq_datasets / geo_search_datasets -- GEO (largest RNA-seq repo)OmicsDI_search_datasets -- cross-repository aggregation (GEO+ArrayExpress+PRIDE+MassIVE)GTEx_get_expression_summary -- baseline tissue expression (54 normal tissues, param: gene_symbol)ENAPortal_search_studies -- sequencing studies (param: query with description="...")CxGDisc_search_datasets -- single-cell datasets (needs exact disease ontology terms)PubMed_search_articles -- dataset discovery via publicationsArrayExpress: keywords (free text), species (scientific name), array (platform filter), limit
BioStudies: query (free text), limit
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
mattpocock/skills
tooluniverse-expression-data-retrieval reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in tooluniverse-expression-data-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
tooluniverse-expression-data-retrieval reduced setup friction for our internal harness; good balance of opinion and flexibility.
tooluniverse-expression-data-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
tooluniverse-expression-data-retrieval fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend tooluniverse-expression-data-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-expression-data-retrieval has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added tooluniverse-expression-data-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added tooluniverse-expression-data-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend tooluniverse-expression-data-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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