Embl Ebi Ols logo

Embl Ebi Ols

OrganizationPopular
google-deepmind
embl-ebi-ols

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.

Overview

Publishergoogle-deepmind
Repositoryscience-skills
Skill nameembl-ebi-ols
Stars
3.1K
Forks
349
Bundled files
10
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google-deepmind on GitHub. Read the source before you install it.

Installation

Install the Embl Ebi Ols AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google-deepmind/science-skills.git /tmp/science-skills
mkdir -p .claude/skills
cp -r /tmp/science-skills/skills/embl_ebi_ols .claude/skills/embl-ebi-ols
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Embl Ebi Ols in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Embl Ebi Ols on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Embl Ebi Ols is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 .licenses/embl_ebi_ols_LICENSE.txt does not already exist in the workspace root 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.

bash
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.

bash
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.

bash
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.

bash
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.

bash
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).

bash
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.

bash
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).

bash
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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Embl Ebi Ols AI skill do?

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.

Why use Embl Ebi Ols on TypingMind?

Because you install it once and use it with any model. Embl Ebi Ols is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Embl Ebi Ols in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google-deepmind/science-skills/tree/main/skills/embl_ebi_ols. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Embl Ebi Ols?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Embl Ebi Ols?

As many as you like. As long as a model supports skills, you can use Embl Ebi Ols with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Embl Ebi Ols AI skill free?

Yes. It is published on GitHub by google-deepmind under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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