The European Nucleotide Archive (ENA) is a comprehensive public repository for nucleotide sequence data and associated metadata. Access and query DNA/RNA sequences, raw reads, genome assemblies, and functional annotations through REST APIs and FTP for genomics and bioinformatics pipelines.
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Create detailed user stories, acceptance criteria, and feature specs
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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
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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
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Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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The European Nucleotide Archive (ENA) is a comprehensive public repository for nucleotide sequence data and associated metadata. Access and query DNA/RNA sequences, raw reads, genome assemblies, and functional annotations through REST APIs and FTP for genomics and bioinformatics pipelines.
This skill should be used when:
ENA organizes data into hierarchical object types:
Studies/Projects - Group related data and control release dates. Studies are the primary unit for citing archived data.
Samples - Represent units of biomaterial from which sequencing libraries were produced. Samples must be registered before submitting most data types.
Raw Reads - Consist of:
Assemblies - Genome, transcriptome, metagenome, or metatranscriptome assemblies at various completion levels.
Sequences - Assembled and annotated sequences stored in the EMBL Nucleotide Sequence Database, including coding/non-coding regions and functional annotations.
Analyses - Results from computational analyses of sequence data.
Taxonomy Records - Taxonomic information including lineage and rank.
ENA provides multiple REST APIs for data access. Consult references/api_reference.md for detailed endpoint documentation.
Key APIs:
ENA Portal API - Advanced search functionality across all ENA data types
ENA Browser API - Direct retrieval of records and metadata
ENA Taxonomy REST API - Query taxonomic information
ENA Cross Reference Service - Access related records from external databases
CRAM Reference Registry - Retrieve reference sequences
Rate Limiting: All APIs have a rate limit of 50 requests per second. Exceeding this returns HTTP 429 (Too Many Requests).
Browser-Based Search:
Programmatic Queries:
Example API Query Pattern:
import requests
# Search for samples from a specific study
base_url = "https://www.ebi.ac.uk/ena/portal/api/search"
params = {
"result": "sample",
"query": "study_accession=PRJEB1234",
"format": "json",
"limit": 100
}
response = requests.get(base_url, params=params)
samples = response.json()
Metadata Formats:
Sequence Data:
Download Methods:
Retrieve raw sequencing reads by accession:
# Download run files using Browser API
accession = "ERR123456"
url = f"https://www.ebi.ac.uk/ena/browser/api/xml/{accession}"
Search for all samples in a study:
# Use Portal API to list samples
study_id = "PRJNA123456"
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=sample&query=study_accession={study_id}&format=tsv"
Find assemblies for a specific organism:
# Search assemblies by taxonomy
organism = "Escherichia coli"
url = f"https://www.ebi.ac.uk/ena/portal/api/search?result=assembly&query=tax_tree({organism})&format=json"
Get taxonomic lineage:
# Query taxonomy API
taxon_id = "562" # E. coli
url = f"https://www.ebi.ac.uk/ena/taxonomy/rest/tax-id/{taxon_id}"
Bulk Download Pattern:
BLAST Integration: Integrate with EBI's NCBI BLAST service (REST/SOAP API) for sequence similarity searches against ENA sequences.
Rate Limiting:
Data Citation:
API Response Handling:
Performance:
This skill includes detailed reference documentation for working with ENA:
api_reference.md - Comprehensive API endpoint documentation including:
Load this reference when constructing complex API queries, debugging API responses, or needing specific parameter details.
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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parcadei/continuous-claude-v3
cursor/plugins
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ailabs-393/ai-labs-claude-skills
Useful defaults in ena-database — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
ena-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added ena-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for ena-database matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend ena-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: ena-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
ena-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: ena-database is focused, and the summary matches what you get after install.
ena-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend ena-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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