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embl-ebi-ols

google-deepmind/science-skills · updated Jun 4, 2026

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$npx skills add https://github.com/google-deepmind/science-skills --skill embl-ebi-ols
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summary

### Embl Ebi Ols

  • name: "embl-ebi-ols"
  • description: "Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search fo..."
skill.md
name
embl-ebi-ols
description
> Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.

EMBL-EBI Ontology Lookup Service (OLS)

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/ols4/api-docs, then (2) create the file recording the notification text and timestamp.

Core Rules

  • [!IMPORTANT] Use the Utility Scripts: You MUST ALWAYS use the provided utility script under scripts/ for all API interactions, including checking status. NEVER use curl or custom Python requests to query API directly.

  • Rate Limiting & Resilience: You MUST respect EBI's Terms of Use with a maximum 5 requests per second. The provided utility scripts automatically enforce this.

  • Notification: If this skill is used, ensure this is mentioned in the output.

When to Use — Quick Recipes

Use this skill whenever a user query matches one of these patterns:

  • Definition of a disease, phenotype, or term → get_term.py --obo_id <ID> --summary
  • Subtypes or children of a term → get_term.py --obo_id <ID> --relations children
  • Parent of a term → get_term.py --obo_id <ID> --relations parents
  • Ancestors / disease categories / classified underget_term.py --obo_id <ID> --relations ancestors
  • Root terms of an ontology → get_term.py --ontology <id> --roots
  • Hierarchical parents (is-a + part-of) → get_term.py --obo_id <ID> --relations hierarchicalParents
  • Structures part of / hierarchical children → get_term.py --obo_id <ID> --relations hierarchicalChildren
  • Compare direct vs hierarchical parents → get_term.py --obo_id <ID> --relations parents,hierarchicalParents
  • Search for a term (e.g., "apoptosis" in GO) → search_ols.py --query "..." --ontology <id>
  • Find a GO term matching a function → search_ols.py --query "..." --ontology go --exact
  • Search in MONDO, CHEBI, CL, UBERONsearch_ols.py --query "..." --ontology <id> --defining
  • Paginate search results / next page → search_ols.py --query "..." --rows N --start <offset>
  • Autocomplete a partial name → suggest_ols.py --query "..."
  • Ontology metadata (e.g., EFO info) → get_ontology.py --id <id>
  • OLS index statistics → get_stats.py

Multi-step queries (e.g., "What is the parent of myocardial infarction?"): When the user names a term but you don't know its OBO ID, complete in exactly 2 steps — do NOT search across multiple ontologies:

  1. Search in the single most appropriate ontology: search_ols.py --query "myocardial infarction" --ontology doid --exact --rows 1 --output /tmp/step1.json
  2. Get relations using the OBO ID from step 1: get_term.py --obo_id DOID:5844 --relations parents --output /tmp/step2.json

Ontology selection rule: ALWAYS use doid for common human diseases (e.g., diabetes, cancer), hp for phenotypes, go for gene functions, chebi for chemicals, uberon for anatomy, cl for cell types. Use mondo ONLY when cross-species context is explicitly mentioned or needed.

Utility Scripts

1. Search Terms Across Ontologies

Search for ontology terms by keyword and return clean JSON.

uv run scripts/search_ols.py --query "diabetes" \
  --rows 5 --output /tmp/ols_search_results.json 2>/dev/null

Important: --output is required for all scripts. Results are always written to the specified file. For larger output, you can limit --rows (e.g., 5-10) or paginate using --start.

Returned Fields: JSON results include iri, label, description, ontology_name, ontology_prefix, obo_id, short_form, type, is_defining_ontology, and exact_synonyms.

Pagination: Output includes a pagination block with start, rows, and has_more so you can decide whether to fetch more results.

Options:

  • --query: Search string (required). Searches labels, synonyms, descriptions, and identifiers.
  • --ontology: Filter by ontology ID (e.g., go, doid, efo, hp). Recommended when you know which ontology to search — avoids noise from 250+ ontologies.
  • --type: Filter by entity type: class, property, individual, or ontology.
  • --exact: Flag for exact label match only. Use this for entity resolution when mapping a user's string to a specific ontology term ID.
  • --defining: Only return terms from their defining (authoritative) ontology. E.g., GO:0005634 only from GO, not cross-referenced copies.
  • --obsolete: Flag to include obsolete terms in results.
  • --local: Only return terms in their defining ontology.
  • --childrenOf: Restrict to children of given term IRI(s), comma-separated.
  • --allChildrenOf: Restrict to all children including transitive relations (part of, develops from), comma-separated IRIs.
  • --queryFields: Comma-separated fields to search in (e.g., label,synonym,description).
  • --fieldList: Comma-separated fields to return.
  • --groupField: Group results by unique IRI.
  • --isLeaf: Only return leaf terms (no children).
  • --rows: Number of results to return (default 10).
  • --start: Pagination offset (default 0).
  • --output: File path to save results (required).

2. Autocomplete / Suggest

Get autocomplete suggestions for partial term names.

uv run scripts/suggest_ols.py --query "diabet" --rows 5 \
  --output /tmp/ols_suggest.json 2>/dev/null

Options:

  • --query: Partial term to autocomplete (required).
  • --ontology: Filter by ontology ID(s), comma-separated.
  • --rows: Number of suggestions (default 10).
  • --start: Pagination offset (default 0).
  • --output: File path to save results (default: stdout).

3. Get Term Details

Retrieve full details for a specific ontology term by its OBO ID or IRI.

uv run scripts/get_term.py --obo_id "GO:0005634" \
  --output /tmp/ols_term.json 2>/dev/null

Returned Fields: JSON includes iri, label, description, obo_id, synonyms, ontology_name, is_obsolete, is_defining_ontology, has_children, is_root, annotation, in_subset, and any requested relations.

Summary Mode: Use --summary to get a clean, human-readable block on stdout (Label, OBO ID, Ontology, Definition, Synonyms). The full JSON is always saved to the --output file.

uv run scripts/get_term.py --obo_id "GO:0005634" --summary \
  --output /tmp/nucleus_full.json

Options:

  • --obo_id: OBO-style identifier (e.g., GO:0005634, DOID:9351). Mutually exclusive with --iri. Auto-converts to IRI with double encoding.

  • --iri: Full IRI of the term. Mutually exclusive with --obo_id.

  • --ontology: Ontology ID (auto-derived from --obo_id if not provided).

  • --relations: Comma-separated list of relations to fetch.

    • Direct (is-a only): parents, children, ancestors, descendants
    • Hierarchical (is-a + transitive like "part of", "develops from"): hierarchicalParents, hierarchicalChildren, hierarchicalAncestors, hierarchicalDescendants
    • Graph: graph — full graph JSON for a term

    Note: Use hierarchical variants for anatomical/developmental ontologies (UBERON, CL) where transitive relations like "part of" and "develops from" are critical for navigating the hierarchy.

  • --roots: List root terms of the ontology (requires --ontology).

  • --preferred_roots: List preferred root terms (requires --ontology).

  • --summary: Human-readable summary on stdout, full JSON to --output.

  • --output: File path to save results (default: stdout).

4. Get Property Details

Retrieve details for an ontology property (relation type) with hierarchy.

uv run scripts/get_property.py --obo_id "BFO:0000051" --ontology go \
  --output /tmp/ols_property.json 2>/dev/null

Options:

  • --obo_id: OBO-style ID of the property. Mutually exclusive with --iri.
  • --iri: Full IRI of the property. Mutually exclusive with --obo_id.
  • --ontology: Ontology ID (required with --iri).
  • --relations: Comma-separated: parents, children, ancestors, descendants.
  • --roots: List root properties of the ontology (requires --ontology).
  • --output: File path to save results (default: stdout).

5. Get Individual Details

Retrieve details for an ontology individual (instance).

uv run scripts/get_individual.py --obo_id "IAO:0000103" --ontology iao --types \
  --output /tmp/ols_individual.json 2>/dev/null

Options:

  • --obo_id: OBO-style ID. Mutually exclusive with --iri.
  • --iri: Full IRI. Mutually exclusive with --obo_id.
  • --ontology: Ontology ID (required with --iri).
  • --types: Fetch the direct types (classes) of this individual.
  • --alltypes: Fetch all types including ancestor classes.
  • --output: File path to save results (default: stdout).

6. Get Ontology Information

List available ontologies or retrieve details for a specific one.

uv run scripts/get_ontology.py --id go \
  --output /tmp/ols_ontology.json 2>/dev/null

Options:

  • --id: Specific ontology ID (e.g., go, efo, doid). If omitted, lists all ontologies.
  • --page: Page number for pagination (default 0).
  • --size: Number of ontologies per page (default 20).
  • --output: File path to save results (default: stdout).

7. Get OLS Statistics

Retrieve index statistics (total ontologies, classes, properties, individuals).

uv run scripts/get_stats.py --output /tmp/ols_stats.json 2>/dev/null

Options:

  • --output: File path to save results (default: stdout).

Reference

Workflow

  1. Use suggest_ols.py for autocomplete when you have a partial term name.
  2. Search for terms using search_ols.py. Use --defining to prioritize authoritative definitions. Use --exact for entity resolution.
  3. If full details are needed, use get_term.py with the OBO ID or IRI. Use --summary for a concise view.
  4. To explore a term's hierarchy, use get_term.py --relations parents,children for is-a only, or --relations hierarchicalParents,hierarchicalChildren for "part of" etc.
  5. To explore from the top down, use get_term.py --ontology go --roots.
  6. For properties or individuals, use get_property.py or get_individual.py.
  7. To discover available ontologies, use get_ontology.py.
  8. To check OLS index status, use get_stats.py.
how to use embl-ebi-ols

How to use embl-ebi-ols on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add embl-ebi-ols
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/google-deepmind/science-skills --skill embl-ebi-ols

The skills CLI fetches embl-ebi-ols from GitHub repository google-deepmind/science-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/embl-ebi-ols

Reload or restart Cursor to activate embl-ebi-ols. Access the skill through slash commands (e.g., /embl-ebi-ols) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.757 reviews
  • Li Jackson· Dec 28, 2024

    embl-ebi-ols reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kabir Garcia· Dec 24, 2024

    Registry listing for embl-ebi-ols matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Dec 16, 2024

    embl-ebi-ols fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Fatima Sethi· Dec 12, 2024

    Solid pick for teams standardizing on skills: embl-ebi-ols is focused, and the summary matches what you get after install.

  • Chinedu Martin· Dec 8, 2024

    We added embl-ebi-ols from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Kofi Anderson· Dec 8, 2024

    I recommend embl-ebi-ols for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chinedu Malhotra· Nov 27, 2024

    embl-ebi-ols reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aditi Gonzalez· Nov 27, 2024

    Keeps context tight: embl-ebi-ols is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aanya Lopez· Nov 19, 2024

    We added embl-ebi-ols from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chen Abebe· Nov 15, 2024

    embl-ebi-ols fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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