oregano-query
Query the OREGANO knowledge graph for computational drug repurposing. Use whenever the user asks about drug–target–disease–gene–pathway relationships, compound cross-references, drug repurposing hypotheses, or wants to explore neighbors of any biomedical entity in a knowledge graph that includes natural compounds.
npx skills add BioTender-max/awesome-bio-agent-skills --skill oregano --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# OREGANO Query Skill Search the OREGANO knowledge graph (88,937 nodes, 824,231 links) by any entity. Auto-resolves input to OREGANO node IDs via cross-reference tables. | Input Pattern | Detected As | Match Logic | |---|---|---| | OREGANO internal ID (e.g. `1234`) | OREGANO node ID | exact in triplet index | | `DB00331` / DrugBank ID | external xref | exact in COMPOUND.tsv | | UniProt / KEGG / MeSH / UMLS ID | external xref | exact across all metadata TSVs | | `metformin`, `BRCA1`, free text | entity name | substring on name columns | ## API | Function | Input | Returns | |---|---|---| | `search(query)` | single entity string | dict: `{query, resolved_ids, metadata, triplets}` | | `search_batch(queries)` | list of entity strings | `dict[str, search_result]` | | `summarize(result)` | search result dict | compact LLM-readable text | | `to_json(result)` | search result dict | JSON-serializable dict | | `get_stats()` | — | graph-level counts (triplets, nodes, predicates, entity types) | ## Graph Schema **11 node types**: Compound (90,868), Gene (35,794), Target (22,096), Disease (18,333), Phenotype (11,605), Side Effect (6,060), Indication (2,714), Pathway (2,129), Effect (171), Activ
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What does the oregano-query skill do?
Query the OREGANO knowledge graph for computational drug repurposing. Use whenever the user asks about drug–target–disease–gene–pathway relationships, compound cross-references, drug repurposing hypotheses, or wants to explore neighbors of any biomedical entity in a knowledge graph that includes natural compounds.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill oregano --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.