wikidata-search

Search for items and properties on Wikidata and retrieve entity details, claims, and external identifiers. Supports both keyword search (Wikidata Action API) and semantic/hybrid search (Wikidata Vector Database), plus direct entity retrieval (Special:EntityData) and structured querying (WDQS SPARQL).

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仓库: kltng/humanities-skills
最近来源活动: 2026年3月6日 20:58
检测到的 SKILL.md 语言: 英语
星标: 7
分支: 4

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npx skills add https://github.com/kltng/humanities-skills --skill wikidata-search

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SKILL.md

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name: wikidata-search
description: Search for items and properties on Wikidata and retrieve entity details, claims, and external identifiers. Supports both keyword search (Wikidata Action API) and semantic/hybrid search (Wikidata Vector Database), plus direct entity retrieval (Special:EntityData) and structured querying (WDQS SPARQL).
version: 1.1.0

# 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.** ```bash 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 ```bash 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 ```python # 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): ```python 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-After` headers - **Action API**: Use `maxlag` parameter; 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 methods - `scripts/wikidata_api.py` — Full-featured Python client with rate limiting, retries, and identifier extraction

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tgaz-query

Query and explore the TGAZ (Temporal Gazetteer) SQLite database of 82,000+ historical Chinese placenames spanning 763 BCE to 1911 CE. Use this skill whenever the user asks about historical Chinese places, administrative geography, dynastic jurisdictions, place name evolution, or wants to query tgaz.db. Also trigger when the user mentions CHGIS, TGAZ, historical gazetteer, Chinese historical GIS, or asks questions like "what was X called in dynasty Y", "what counties existed in year Z", "where was X located", or any spatial/temporal query about Chinese historical geography. This skill is relevant even for casual questions like "tell me about ancient Chang'an" or "Tang dynasty cities near the Yellow River".

2026-08-18

cbdb-local

Query the China Biographical Database (CBDB) locally via SQLite for biographical data on 656K+ historical Chinese figures from the 7th century BCE through the 19th century CE. Use when searching for Chinese historical figures, scholars, officials, or literary figures — their biographical details, family/kinship networks, official postings, social associations, examination records, or addresses. Runs entirely locally after initial database download (~556 MB). Faster and more flexible than the API version.

2026-05-28

human-eval-review

Generate a single self-contained HTML file for human-in-the-loop evaluation of model output. The reviewer opens it in a browser, makes per-item judgments (pick the matching candidate(s), choose one, rate on a scale, yes/no, or free-text correction), and exports their decisions as JSON. Use when you have analysis output — candidate matches, rankings, classifications, translations, extractions — that a human needs to review, confirm, score, or correct.

2026-05-27

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