UniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot/TrEMBL annotations via REST API for protein analysis.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionuniprot-databaseExecute the skills CLI command in your project's root directory to begin installation:
Fetches uniprot-database from davila7/claude-code-templates 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 uniprot-database. Access via /uniprot-database 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.
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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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UniProt is the world's leading comprehensive protein sequence and functional information resource. Search proteins by name, gene, or accession, retrieve sequences in FASTA format, perform ID mapping across databases, access Swiss-Prot/TrEMBL annotations via REST API for protein analysis.
This skill should be used when:
Search UniProt using natural language queries or structured search syntax.
Common search patterns:
# Search by protein name
query = "insulin AND organism_name:\"Homo sapiens\""
# Search by gene name
query = "gene:BRCA1 AND reviewed:true"
# Search by accession
query = "accession:P12345"
# Search by sequence length
query = "length:[100 TO 500]"
# Search by taxonomy
query = "taxonomy_id:9606" # Human proteins
# Search by GO term
query = "go:0005515" # Protein binding
Use the API search endpoint: https://rest.uniprot.org/uniprotkb/search?query={query}&format={format}
Supported formats: JSON, TSV, Excel, XML, FASTA, RDF, TXT
Retrieve specific protein entries by accession number.
Accession number formats:
Retrieve endpoint: https://rest.uniprot.org/uniprotkb/{accession}.{format}
Example: https://rest.uniprot.org/uniprotkb/P12345.fasta
Map protein identifiers between different database systems and retrieve multiple entries efficiently.
ID Mapping workflow:
https://rest.uniprot.org/idmapping/runhttps://rest.uniprot.org/idmapping/status/{jobId}https://rest.uniprot.org/idmapping/results/{jobId}Supported databases for mapping:
/references/id_mapping_databases.md)Limitations:
For large queries that exceed pagination limits, use the stream endpoint:
https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}
The stream endpoint returns all results without pagination, suitable for downloading complete datasets.
Specify exactly which fields to retrieve for efficient data transfer.
Common fields:
accession - UniProt accession numberid - Entry namegene_names - Gene name(s)organism_name - Organismprotein_name - Protein namessequence - Amino acid sequencelength - Sequence lengthgo_* - Gene Ontology annotationscc_* - Comment fields (function, interaction, etc.)ft_* - Feature annotations (domains, sites, etc.)Example: https://rest.uniprot.org/uniprotkb/search?query=insulin&fields=accession,gene_names,organism_name,length,sequence&format=tsv
See /references/api_fields.md for complete field list.
For programmatic access, use the provided helper script scripts/uniprot_client.py which implements:
search_proteins(query, format) - Search UniProt with any queryget_protein(accession, format) - Retrieve single protein entrymap_ids(ids, from_db, to_db) - Map between identifier typesbatch_retrieve(accessions, format) - Retrieve multiple entriesstream_results(query, format) - Stream large result setsAlternative Python packages:
Boolean operators:
kinase AND organism_name:human
(diabetes OR insulin) AND reviewed:true
cancer NOT lung
Field-specific searches:
gene:BRCA1
accession:P12345
organism_id:9606
taxonomy_name:"Homo sapiens"
annotation:(type:signal)
Range queries:
length:[100 TO 500]
mass:[50000 TO 100000]
Wildcards:
gene:BRCA*
protein_name:kinase*
See /references/query_syntax.md for comprehensive syntax documentation.
reviewed:true for Swiss-Prot (manually curated) entriesuniprot_client.py - Python client with helper functions for common UniProt operations including search, retrieval, ID mapping, and streaming.
api_fields.md - Complete list of available fields for customizing queriesid_mapping_databases.md - Supported databases for ID mapping operationsquery_syntax.md - Comprehensive query syntax with advanced examplesapi_examples.md - Code examples in multiple languages (Python, curl, R)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.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
uniprot-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: uniprot-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: uniprot-database is focused, and the summary matches what you get after install.
uniprot-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
uniprot-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added uniprot-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in uniprot-database — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend uniprot-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend uniprot-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added uniprot-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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