Wikidata Search Skill
Search and retrieve data from Wikidata, the free knowledge base.
Critical: Things Claude Won't Know Without This Skill
Wikidata Vector Database (semantic search)
This is the highest-value feature of this skill. The Wikidata Vector Database at wd-vectordb.wmcloud.org provides semantic/hybrid search over all Wikidata items — something you can't do with the standard Action API or SPARQL.
A descriptive User-Agent header is required or you get 403.
curl -H 'User-Agent: WikidataSearchSkill/1.0 (contact: you@example.com)' \
'https://wd-vectordb.wmcloud.org/item/query/?query=historical+Chinese+cartography&lang=all&K=20'Response includes QID, similarity_score, rrf_score, and source (vector vs keyword).
Property search: replace /item/query/ with /property/query/.
Optional params: lang, K (result count), instanceof (comma-separated QIDs), rerank.
WDQS SPARQL also requires User-Agent
curl -G 'https://query.wikidata.org/sparql' \
--data-urlencode 'query=SELECT ?item ?label WHERE { ?item wdt:P31 wd:Q12857432 . ?item rdfs:label ?label . FILTER(LANG(?label)="en") }' \
-H 'Accept: application/sparql-results+json' \
-H 'User-Agent: WikidataSearchSkill/1.0 (contact: you@example.com)'External identifiers live in claims
# claims[property_id][0]["mainsnak"]["datavalue"]["value"] → identifier string
# Common: P214 (VIAF), P244 (LoC), P227 (GND), P213 (ISNI), P268 (BnF)Choosing an Access Method
| Need | Method |
|---|---|
| Keyword search by label/alias | Action API wbsearchentities |
| Semantic / fuzzy concept discovery | Vector Database (hybrid vector + keyword) |
| Fetch a known entity's JSON | Special:EntityData/{ID}.json |
| Complex graph queries / reporting | WDQS SPARQL |
Python Script
Use scripts/wikidata_api.py for programmatic access (zero dependencies):
from scripts.wikidata_api import WikidataAPI
wd = WikidataAPI()
# Keyword search
results = wd.search("Zhu Xi", language="en", limit=5)
# Semantic search (Vector DB) — the key differentiator
candidates = wd.vector_search_items("historical Chinese cartography", lang="all", k=20)
# Entity retrieval
entity = wd.get_entity("Q9397", props=["labels", "descriptions", "claims"])
# External identifiers
ids = wd.get_identifiers("Q9397", include_labels=True)
# → {'VIAF ID (P214)': '46768804', 'Library of Congress ID (P244)': 'n81008179', ...}
# SPARQL
results = wd.sparql_json("SELECT ?item ?label WHERE { ?item wdt:P31 wd:Q12857432 . ?item rdfs:label ?label . FILTER(LANG(?label)='en') }")
# Direct entity JSON (fast for current state)
data = wd.get_entitydata("Q42", flavor="simple")API Endpoints Quick Reference
| Endpoint | URL |
|---|---|
| Action API | https://www.wikidata.org/w/api.php |
| Entity JSON | https://www.wikidata.org/wiki/Special:EntityData/{ID}.json |
| SPARQL | https://query.wikidata.org/sparql |
| Vector DB | https://wd-vectordb.wmcloud.org |
API Etiquette
- Rate limit: 0.5–1s between requests
- User-Agent: Required for Vector DB and WDQS (include contact info)
- Respect 429: Honor
Retry-Afterheaders - Action API: Use
maxlagparameter; batch with pipe-separated IDs (max 50) - SPARQL: Request only needed fields; use
LIMIT
Related Skills
- cbdb-api: Cross-reference Wikidata entities with CBDB biographical data for Chinese historical figures
- chgis-tgaz: Look up historical places found via Wikidata in the CHGIS Temporal Gazetteer for detailed administrative history
Resources
references/api_reference.md— Complete API specs for all four access methodsscripts/wikidata_api.py— Full-featured Python client with rate limiting, retries, and identifier extraction