qsong-github/drugclaw/chebi
Claude Code and agent skill by qsong-github/drugclaw
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Query the ChEBI (Chemical Entities of Biological Interest) database. Use whenever the user asks about small molecule identifiers, chemical ontology roles, molecular formulae, SMILES, InChI, synonyms, or cross-references for biologically relevant chemical compounds via ChEBI.
Install
npx vskill@latest install qsong-github/drugclaw/chebiSKILL.md
The same SKILL.md was published earlier as majiayu000/claude-skill-registry-data/chebi.
ChEBI Query Skill
Search the ChEBI 2.0 REST API by any entity. Auto-detects input type:
| Input Pattern | Detected As | Action | |
|---|---|---|---|
CHEBI:15422 / chebi:15422 | ChEBI ID (prefixed) | full entity lookup via /compound/{id}/ | |
27732 (pure digits ≤7) | ChEBI ID (numeric) | full entity lookup via /compound/{id}/ | |
| anything else | free text | Elasticsearch search via /es_search/?term=... |
API
| Function | Input | Returns | |
|---|---|---|---|
search(query, max_results=25) | single entity string | list[dict] | |
search_batch(queries, max_results=25) | list of entity strings | dict[str, list[dict]] | |
summarize(results, label) | result list + label | compact one-line-per-hit text | |
to_json(results) | result list | list[dict] (JSON-serialisable) |
Lower-level helpers (called internally):
| Function | Purpose | |
|---|---|---|
search_chebi(query, max_results) | keyword search via GET /es_search/?term=...&size=N | |
get_entity(chebi_id) | full entity via GET /compound/CHEBI:{id}/ | |
get_entities_batch(chebi_ids) | batch lookup via GET /compounds/?chebi_ids=id1,id2,... |
search_batch() automatically uses the efficient batch endpoint when all queries are ChEBI IDs; otherwise it iterates with a 0.3 s delay.
Usage
See if __name__ == "__main__" block in 64_ChEBI.py for runnable examples covering: single ID lookup, name search, formula search, batch ID search, mixed batch search, and JSON output.
Key Fields Returned
Top-level: chebi_accession, name, ascii_name, definition, stars (curation quality; 3 = fully curated), secondary_ids, is_released.
chemical_data (nested dict): formula, charge, mass, monoisotopic_mass.
default_structure (nested dict): smiles, standard_inchi, standard_inchi_key, wurcs.
names (nested dict by type): keys like IUPAC NAME, SYNONYM, BRAND NAME, UNIPROT NAME; each value is a list of name objects.
ontology_relations: incoming_relations and outgoing_relations, each a list of {init_id, init_name, relation_type, final_id, final_name}.
database_accessions (nested dict by type): CAS, MANUAL_X_REF, REGISTRY_NUMBER, CITATION; cross-refs to DrugBank, KEGG, HMDB, PubChem, PDBeChem, Wikipedia, etc.
roles_classification: list of role dicts with chebi_accession, name, definition, biological_role (bool), application (bool), chemical_role (bool).
Data Source
- Database: ChEBI 2.0 (EMBL-EBI), >195,000 molecular entities
- API base:
https://www.ebi.ac.uk/chebi/backend/api/public/ - Docs: <https://www.ebi.ac.uk/chebi/backend/api/docs/>
- License: CC BY 4.0
- Citation: Bento et al. Nucleic Acids Res. 2025, 54(D1), D1768–D1774.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/drug_ontology/chebi/SKILL.md
Popularity
- Repository stars
- 117
- Repository forks
- 3
- 7-day trend score
- 0
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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