Retrieve DNA, RNA, and protein sequences from NCBI and ENA with automatic gene disambiguation and cross-database handling.
Works with
Searches NCBI Nucleotide by organism, gene name, strain, and sequence type; automatically disambiguates genes across species and resolves accession prefixes to the correct database
Handles RefSeq (NC_, NM_, NP_) and GenBank accessions with intelligent fallback between NCBI and ENA; never attempts ENA queries on RefSeq-only accessions
Returns detailed sequence pro
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-sequence-retrievalExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-sequence-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-sequence-retrieval. Access via /tooluniverse-sequence-retrieval 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.
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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 DNA, RNA, and protein sequences with proper disambiguation and cross-database handling.
IMPORTANT: Always use English terms in tool calls. Only try original-language terms as fallback. Respond in the user's language.
LOOK UP DON'T GUESS: Never assume accession numbers or sequence versions. Always retrieve and verify from NCBI or ENA.
Sequence quality hierarchy: RefSeq (NM_/NP_ = curated) > RefSeq predicted (XM_/XP_) > GenBank (submitted). Prefer the MANE Select transcript for human canonical isoforms. Check version numbers -- annotations improve across versions.
Phase 0: Clarify (if needed) → Phase 1: Disambiguate Gene/Organism → Phase 2: Search & Retrieve → Phase 3: Report
Ask ONLY if: gene exists in multiple organisms, sequence type unclear, or strain matters. Skip for: specific accessions, clear organism+gene combos, complete genome requests with organism.
| Prefix | Type | Use With |
|---|---|---|
| NC_/NM_/NR_/NP_/XM_ | RefSeq | NCBI only |
| U*/M*/K*/X*/CP*/NZ_ | GenBank | NCBI or ENA |
| EMBL format | EMBL | ENA preferred |
CRITICAL: Never try ENA tools with RefSeq accessions -- they return 404.
Retrieve silently. Do NOT narrate the search process.
# Search NCBI Nucleotide
result = tu.tools.NCBI_search_nucleotide(
operation="search", organism=organism, gene=gene,
strain=strain, keywords=keywords, seq_type=seq_type, limit=10
)
# Get accessions from UIDs
accessions = tu.tools.NCBI_fetch_accessions(operation="fetch_accession", uids=result["data"]["uids"])
# Retrieve sequence (FASTA or GenBank format)
sequence = tu.tools.NCBI_get_sequence(operation="fetch_sequence", accession=accession, format="fasta")
# ENA alternative (non-RefSeq accessions only)
entry = tu.tools.ena_get_entry(accession=accession)
fasta = tu.tools.ena_get_sequence_fasta(accession=accession)
| Primary | Fallback | Notes |
|---|---|---|
| NCBI_get_sequence | ENA (if GenBank format) | NCBI unavailable |
| ENA_get_entry | NCBI_get_sequence | ENA doesn't have RefSeq |
| NCBI_search_nucleotide | Try broader keywords | No results |
Present as a Sequence Profile Report. Hide search process. Include:
| Tier | Prefix | Description |
|---|---|---|
| RefSeq Reference (best) | NC_, NM_, NP_ | NCBI-curated, gold standard |
| RefSeq Predicted | XM_, XP_, XR_ | Computationally predicted |
| GenBank Validated | Various | Submitted, some curation |
| GenBank Direct | Various | Direct submission |
| Third Party | TPA_ | Third-party annotation |
Sequence quality: Prefer RefSeq over GenBank. Check version numbers. Sequences with "PREDICTED" in definition are not experimentally validated.
Accession guidance: RefSeq = NCBI-only. GenBank = mirrored in ENA/EMBL. Default to RefSeq mRNA (NM_) for human/model organisms; most complete genome assembly for microbial queries.
Cross-database reconciliation: Same sequence may have different accessions (e.g., GenBank U00096 = RefSeq NC_000913 for E. coli K-12). Always report both when available. Discrepancies between GenBank/RefSeq typically indicate RefSeq curation corrected submission errors.
| Error | Response |
|---|---|
| "No search criteria provided" | Add organism, gene, or keywords |
| "ENA 404 error" | Likely RefSeq -- use NCBI only |
| "No results found" | Broaden search, check spelling, try synonyms |
| "Sequence too large" | Note size, provide download link instead |
NCBI Tools: NCBI_search_nucleotide (search), NCBI_fetch_accessions (UID→accession), NCBI_get_sequence (retrieve)
ENA Tools (GenBank/EMBL only): ena_get_entry (metadata), ena_get_sequence_fasta (FASTA), ena_get_entry_summary (summary)
NCBI_search_nucleotide: operation="search", organism (scientific name), gene (symbol), strain, keywords, seq_type (complete_genome/mrna/refseq), limit
NCBI_get_sequence: operation="fetch_sequence", accession, format (fasta/genbank)
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.
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I recommend tooluniverse-sequence-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in tooluniverse-sequence-retrieval — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for tooluniverse-sequence-retrieval matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: tooluniverse-sequence-retrieval is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend tooluniverse-sequence-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-sequence-retrieval is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added tooluniverse-sequence-retrieval from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
tooluniverse-sequence-retrieval fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tooluniverse-sequence-retrieval reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend tooluniverse-sequence-retrieval for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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