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デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill open-library-apiコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
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.
SKILL.md を表示中
SOC 職業分類に基づく
| name | open-library-api |
| description | Search and access book metadata via the Open Library API |
| metadata | {"openclaw":{"emoji":"📕","category":"literature","subcategory":"search","keywords":["Open Library","book search","ISBN lookup","Internet Archive","book metadata","digital library"],"source":"https://openlibrary.org/"}} |
Open Library (by the Internet Archive) catalogs every book ever published — 40M+ editions, 20M+ unique works. The API provides book search, ISBN/OCLC lookup, cover images, and reading access for 2M+ borrowable ebooks. Particularly useful for monograph discovery, edition tracking, and bibliographic verification. Free, no authentication required.
# Full-text search
curl "https://openlibrary.org/search.json?q=machine+learning&limit=20"
# Search by title
curl "https://openlibrary.org/search.json?title=deep+learning&limit=10"
# Search by author
curl "https://openlibrary.org/search.json?author=goodfellow&title=deep+learning"
# Filter by subject
curl "https://openlibrary.org/search.json?q=statistics&subject=data+analysis"
# Filter by publication year
curl "https://openlibrary.org/search.json?q=artificial+intelligence&first_publish_year=2020"
# Sort by edition count
curl "https://openlibrary.org/search.json?q=calculus&sort=editions"
# Get work (canonical book entity)
curl "https://openlibrary.org/works/OL45804W.json"
# Get edition
curl "https://openlibrary.org/books/OL7353617M.json"
# Get by ISBN
curl "https://openlibrary.org/isbn/9780262035613.json"
# Bibliographic data via Books API
curl "https://openlibrary.org/api/books?bibkeys=ISBN:9780262035613&format=json&jscmd=data"
# By ISBN (S/M/L sizes)
https://covers.openlibrary.org/b/isbn/9780262035613-M.jpg
# By OLID
https://covers.openlibrary.org/b/olid/OL7353617M-L.jpg
# Get author
curl "https://openlibrary.org/authors/OL34184A.json"
# Search authors
curl "https://openlibrary.org/search/authors.json?q=hinton"
# Author's works
curl "https://openlibrary.org/authors/OL34184A/works.json?limit=20"
| Parameter | Description | Example |
|---|---|---|
q | General search | q=neural+networks |
title | Title search | title=deep+learning |
author | Author search | author=bengio |
subject | Subject filter | subject=computer+science |
isbn | ISBN lookup | isbn=9780262035613 |
first_publish_year | Publication year | first_publish_year=2020 |
limit | Results (max 100) | limit=50 |
offset | Pagination | offset=50 |
sort | Sort order | new, editions, old |
fields | Return fields | key,title,author_name |
{
"numFound": 1250,
"docs": [
{
"key": "/works/OL45804W",
"title": "Deep Learning",
"author_name": ["Ian Goodfellow", "Yoshua Bengio", "Aaron Courville"],
"first_publish_year": 2016,
"isbn": ["9780262035613"],
"publisher": ["MIT Press"],
"subject": ["Machine learning", "Neural networks"],
"edition_count": 8,
"cover_i": 8739161
import requests
BASE_URL = "https://openlibrary.org"
def search_books(query: str, limit: int = 20,
subject: str = None) -> list:
"""Search Open Library for books."""
params = {"q": query, "limit": limit}
if subject:
params["subject"] = subject
resp = requests.get(f"{BASE_URL}/search.json", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for doc in data.get("docs", []):
results.append({
"key": doc.get("key"),
"title": doc.get("title"),
"authors": doc.get("author_name", []),
"year": doc.get("first_publish_year"),
"publisher": doc.get("publisher", [None])[0],
"isbn": doc.get("isbn", [None])[0],
"editions": doc.get("edition_count", 0),
"subjects": doc.get("subject", [])[:5],
"ebook": doc.get("ebook_access"),
})
return results
def () -> :
resp = requests.get(
,
params={
: ,
: ,
: ,
},
)
resp.raise_for_status()
data = resp.json()
data.get(, {})
() -> :
resp = requests.get(
,
params={: limit},
)
resp.raise_for_status()
resp.json().get(, [])
books = search_books(,
subject=)
b books[:]:
()
()
info = get_by_isbn()
()
()