Embl Ebi Ols
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The embl-ebi-ols skill provides an interface to the EMBL-EBI Ontology Lookup Service (OLS), enabling users to programmatically access over 250 biomedical ontologies. It facilitates searching for terms, retrieving detailed metadata, and navigating hierarchical relationships such as parents, children, and ancestors. The skill includes a suite of utility scripts for interacting with the OLS API, supporting tasks ranging from simple term lookups to complex graph structure exploration across standard ontologies like GO, DOID, and HP.
What it does
- Search for biomedical terms across 250+ ontologies
- Retrieve detailed definitions and metadata for specific ontology terms
- Navigate hierarchical structures including parents, children, and ancestors
- Support both direct is-a relationships and transitive hierarchical relations
- Provide autocomplete suggestions for partial term names
- Access ontology-level statistics and metadata
When to use it
- When you need to define a disease, phenotype, or biological term
- When exploring the hierarchy or sub-types of a specific ontology term
- When mapping user-provided strings to authoritative ontology IDs
- When performing cross-ontology research or data annotation
- When you need to identify root terms or property details within an ontology
Advantages
- Enforces rate limiting to respect EBI's Terms of Use
- Provides standardized utility scripts for consistent API interaction
- Supports both OBO ID and IRI formats for flexible querying
- Enables navigation of complex transitive relationships like part-of or develops-from
- Facilitates precise entity resolution using exact match flags
Usage examples
- “What is the definition of the GO term GO:0005634?”
- The agent uses get_term.py to retrieve and display the definition and metadata for the specified Gene Ontology term.
- “Find the parent terms for myocardial infarction in the Disease Ontology.”
- The agent performs a two-step process: searching for the term to get its ID, then querying the OLS for its hierarchical parents.
- “Give me autocomplete suggestions for 'diabet'.”
- The agent uses suggest_ols.py to return a list of potential matches starting with the provided string.
- “List the root terms for the Human Phenotype Ontology.”
- The agent uses get_term.py with the --roots flag to retrieve the top-level terms for the HP ontology.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/embl-ebi-ols/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip embl-ebi-ols.zip -d .agents/skills
Documentation by Google DeepMind
EMBL-EBI Ontology Lookup Service (OLS)
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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 usecurlor 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> --summarySubtypes or children of a term →
get_term.py --obo_id <ID> --relations childrenParent of a term →
get_term.py --obo_id <ID> --relations parentsAncestors / disease categories / classified under →
get_term.py --obo_id <ID> --relations ancestorsRoot terms of an ontology →
get_term.py --ontology <id> --rootsHierarchical parents (is-a + part-of) →
get_term.py --obo_id <ID> --relations hierarchicalParentsStructures part of / hierarchical children →
get_term.py --obo_id <ID> --relations hierarchicalChildrenCompare direct vs hierarchical parents →
get_term.py --obo_id <ID> --relations parents,hierarchicalParentsSearch 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 --exactSearch in MONDO, CHEBI, CL, UBERON →
search_ols.py --query "..." --ontology <id> --definingPaginate 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:
Search in the single most appropriate ontology:
search_ols.py --query "myocardial infarction" --ontology doid --exact --rows 1 --output /tmp/step1.jsonGet relations using the OBO ID from step 1:
get_term.py --obo_id DOID:5844 --relations parents --output /tmp/step2.jsonOntology selection rule: ALWAYS use
doidfor common human diseases (e.g., diabetes, cancer),hpfor phenotypes,gofor gene functions,chebifor chemicals,uberonfor anatomy,clfor cell types. UsemondoONLY 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/nullImportant:
--outputis 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, orontology.--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:0005634only 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/nullOptions:
--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/nullReturned 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.jsonOptions:
--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_idif not provided).--relations: Comma-separated list of relations to fetch.Direct (is-a only):
parents,children,ancestors,descendantsHierarchical (is-a + transitive like "part of", "develops from"):
hierarchicalParents,hierarchicalChildren,hierarchicalAncestors,hierarchicalDescendantsGraph:
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/nullOptions:
--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/nullOptions:
--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/nullOptions:
--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/nullOptions:
--output: File path to save results (default: stdout).
Reference
API Reference: See references/api_reference.md for common ontology IDs, OBO ID format, and key API endpoints.
Workflow
Use
suggest_ols.pyfor autocomplete when you have a partial term name.Search for terms using
search_ols.py. Use--definingto prioritize authoritative definitions. Use--exactfor entity resolution.If full details are needed, use
get_term.pywith the OBO ID or IRI. Use--summaryfor a concise view.To explore a term's hierarchy, use
get_term.py --relations parents,childrenfor is-a only, or--relations hierarchicalParents,hierarchicalChildrenfor "part of" etc.To explore from the top down, use
get_term.py --ontology go --roots.For properties or individuals, use
get_property.pyorget_individual.py.To discover available ontologies, use
get_ontology.py.To check OLS index status, use
get_stats.py.
Files
- ATTRIBUTION.mdAdded by AutoGineer906 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md11.2 KiB
- references/api_reference.md5.0 KiB
- references/citation.bib2.0 KiB
- scripts/get_individual.pyScript5.3 KiB
- scripts/get_ontology.pyScript4.3 KiB
- scripts/get_property.pyScript6.6 KiB
- scripts/get_stats.pyScript2.0 KiB
- scripts/get_term.pyScript10.3 KiB
- scripts/ols_utils.pyScript2.7 KiB
- scripts/search_ols.pyScript6.4 KiB
- scripts/suggest_ols.pyScript3.7 KiB
references/citation.bib
@ARTICLE{Jupp2015,
AUTHOR = {Jupp, Simon and Burdett, Tony and Leroy, Catherine and Parkinson, Helen E},
DATE = {2015},
JOURNALTITLE = {SWAT4LS},
PAGES = {118--119},
TITLE = {A new Ontology Lookup Service at EMBL-EBI.},
VOLUME = {2},
}
@ARTICLE{McLaughlin2025,
ABSTRACT = {The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently, there has been a significant increase in the size and complexity of ontologies due to new scales of biological knowledge, such as spatial transcriptomics, new ontology development methodologies, and curation on an increased scale. Existing Web-based tools for ontology browsing such as BioPortal and OntoBee do not support the full range of definitions used by today’s ontologies. In order to support the community going forward, we have developed OLS4, implementing the complete OWL2 specification, internationalization support for multiple languages, and a new user interface with UX enhancements such as links out to external databases. OLS4 has replaced OLS3 in production at EMBL-EBI and has a backward compatible API supporting users of OLS3 to transition.The source code of OLS is available at https://github.com/EBISPOT/ols4 and DOI 10.5281/zenodo.14960290 with Apache 2.0 License. A freely available implementation is accessible at https://www.ebi.ac.uk/ols4.},
AUTHOR = {McLaughlin, James and Lagrimas, Josh and Iqbal, Haider and Parkinson, Helen and Harmse, Henriette},
URL = {https://doi.org/10.1093/bioinformatics/btaf279},
DATE = {2025-05},
DOI = {10.1093/bioinformatics/btaf279},
EPRINT = {https://academic.oup.com/bioinformatics/article-pdf/41/5/btaf279/63054843/btaf279.pdf},
ISSN = {1367-4811},
JOURNALTITLE = {Bioinformatics},
NUMBER = {5},
PAGES = {btaf279},
TITLE = {OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem},
VOLUME = {41},
}
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/embl_ebi_ols · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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