brycewang-stanford
brycewang-stanford/Auto-Empirical-Research-Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill wikidata-api-guide命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
| name | wikidata-api-guide |
| description | Query Wikidata SPARQL for scholarly metadata, authors, and entities |
| metadata | {"openclaw":{"emoji":"🌐","category":"literature","subcategory":"metadata","keywords":["wikidata","sparql","linked-data","metadata","knowledge-graph","scholarly"],"source":"https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service"}} |
Wikidata is a free, collaborative, multilingual knowledge base maintained by the Wikimedia Foundation. It contains structured data about millions of entities including scholarly articles, academic journals, researchers, universities, and scientific concepts. Each entity has a unique QID and properties linking it to other entities, forming a rich knowledge graph.
For academic researchers, Wikidata serves as a powerful tool for bibliometric analysis, disambiguation of author names, mapping institutional relationships, and linking scholarly outputs across different identifier systems (DOI, ORCID, PubMed ID, arXiv ID, etc.). The SPARQL query service provides a flexible, standards-based interface for complex graph queries.
The Wikidata Query Service is entirely free, requires no authentication, and supports the full SPARQL 1.1 query language. It is especially powerful for cross-referencing scholarly metadata that spans multiple databases and identifier systems.
No authentication is required. The Wikidata SPARQL endpoint is free and open.
# No API key needed -- set a descriptive User-Agent header as courtesy
curl -G "https://query.wikidata.org/sparql" \
--data-urlencode "query=SELECT ?item WHERE { ?item wdt:P31 wd:Q5 } LIMIT 5" \
-H "Accept: application/json" \
-H
GET https://query.wikidata.org/sparql?query={SPARQL}&format=json
Parameters:
query (required): URL-encoded SPARQL queryformat: Response format (json, xml, csv, tsv)curl -G "https://query.wikidata.org/sparql" \
--data-urlencode 'query=
SELECT ?paper ?paperLabel ?doi WHERE {
?author wdt:P496 "0000-0002-1825-0097" .
?paper wdt:P50 ?author ;
wdt:P356 ?doi .
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
} LIMIT 20' \
-H "Accept: application/json" \
-H "User-Agent: ResearchClaw/1.0"
SELECT ?journal ?journalLabel ?issn (COUNT(?article) AS ?articleCount) WHERE {
?journal wdt:P31 wd:Q5633421 ;
wdt:P236 ?issn .
?article wdt:P1433 ?journal .
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
GROUP BY ?journal ?journalLabel ?issn
ORDER BY DESC(?articleCount)
LIMIT 20
import requests
SPARQL_URL = "https://query.wikidata.org/sparql"
HEADERS = {
"Accept": "application/json",
"User-Agent": "ResearchClaw/1.0 (academic research tool)"
}
def query_wikidata(sparql_query):
"""Execute a SPARQL query against Wikidata."""
resp = requests.get(
SPARQL_URL,
params={"query": sparql_query},
headers=HEADERS
)
resp.raise_for_status()
data = resp.json()
return data["results"]["bindings"]
# Find all identifier mappings for a researcher
sparql = """
SELECT ?person ?personLabel ?orcid ?scopus ?dblp ?gscholar WHERE {
?person wdt:P496 "0000-0002-1825-0097" .
OPTIONAL { ?person wdt:P496 ?orcid . }
OPTIONAL { ?person wdt:P1153 ?scopus . }
OPTIONAL { ?person wdt:P2456 ?dblp . }
OPTIONAL { ?person wdt:P1960 ?gscholar . }
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
"""
results = query_wikidata(sparql)
for r in results:
print(f"Name: {r.get('personLabel', {}).get('value', 'N/A')}")
print(f" ORCID: {r.get('orcid', {}).get('value', 'N/A')}")
print(f" Scopus: {r.get('scopus', {}).get('value', 'N/A')}")
print(f" DBLP: {r.get('dblp', {}).get('value', 'N/A')}")
print(f" Google Scholar: {r.get('gscholar', {}).get('value', 'N/A')}")
SELECT ?uni ?uniLabel ?country ?countryLabel ?coord WHERE {
?uni wdt:P31 wd:Q3918 ;
wdt:P17 ?country ;
wdt:P625 ?coord .
FILTER(?country = wd:Q30)
SERVICE wikibase:label { bd:serviceParam wikibase:language "en" . }
}
LIMIT 50
Author Disambiguation: Use Wikidata to resolve author names by cross-referencing ORCID, Scopus ID, DBLP, and Google Scholar identifiers. This is particularly useful when a common name maps to multiple researchers.
Bibliometric Graph Construction: Build citation and co-authorship networks by querying the relationships between authors, papers, journals, and institutions in the Wikidata graph.
Identifier Translation: Convert between DOI, PubMed ID, arXiv ID, and other identifiers using Wikidata's comprehensive property mappings. This enables linking records across heterogeneous databases.
Institutional Analysis: Map university affiliations, geographic distributions, and organizational hierarchies for researchers in a specific field.
SERVICE wikibase:label for human-readable labels instead of QIDs