القائمة
Claude Code skill for citation workflow via OpenAlex and CrossRef
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill citation-assistant-skillيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | citation-assistant-skill |
| description | Claude Code skill for citation workflow via OpenAlex and CrossRef |
| metadata | {"openclaw":{"emoji":"📎","category":"writing","subcategory":"citation","keywords":["citation assistant","OpenAlex","Claude Code skill","reference lookup","academic citation"],"source":"https://github.com/ZhangNy301/citation-assistant"}} |
Citation Assistant is a Claude Code skill that integrates OpenAlex and CrossRef APIs into the coding workflow for instant paper lookup, citation formatting, and reference management. Search for papers by title or keyword, get formatted BibTeX entries, find related works, and insert citations — all without leaving the terminal. Designed for researchers writing papers in LaTeX or Markdown.
# Add as Claude Code skill
# Copy SKILL.md to your Claude Code skills directory
# Or install via OpenClaw:
openclaw skills install citation-assistant
import requests
OA_API = "https://api.openalex.org"
def search_papers(query, limit=5):
"""Search OpenAlex for papers."""
resp = requests.get(
f"{OA_API}/works",
params={
"search": query,
"per_page": limit,
},
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"},
)
return resp.json().get("results", [])
papers = search_papers("attention mechanism transformer")
for p in papers:
authors = [a["author"]["display_name"] for a in p.get("authorships", [])[:3]]
print(f"[{p.get('publication_year')}] {p.get('title')}")
print(f" {', '.join(authors)} — Citations: {p.get('cited_by_count')}")
print(f" DOI: {p.get('doi', 'N/A')}")
def get_bibtex(doi):
"""Get BibTeX for a paper via CrossRef DOI resolution."""
resp = requests.get(
f"https://api.crossref.org/works/{doi}",
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai; mailto:dev@wentor.ai)"},
)
msg = resp.json().get("message", {})
# Generate citation key
authors = msg.get("author", [])
first_author = authors[0].get("family", "unknown").lower() if authors else "unknown"
year = str(msg.get("published", {}).get("date-parts", [[""]])[0][0])
key = f"{first_author}{year}"
# Build BibTeX
authors_str = " and ".join(f"{a.get('given', '')} {a.get('family', '')}".strip() for a in authors)
doi_str = msg.get("DOI", "")
title = msg.get("title", [""])[0] if isinstance(msg.get("title"), list) else msg.get("title", "")
journal = msg.get("container-title", [])[] msg.get()
bibtex =
bibtex
bibtex = get_bibtex()
(bibtex)
def get_citing_works(openalex_id, limit=10):
"""Get papers that cite this work via OpenAlex."""
resp = requests.get(
f"{OA_API}/works",
params={
"filter": f"cites:{openalex_id}",
"per_page": limit,
"sort": "cited_by_count:desc",
},
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"},
)
results = resp.json().get("results", [])
for paper in results:
authors = [a["author"]["display_name"] for a in paper.get("authorships", [])[:3]]
print(f"\n{paper.get('title')} ({paper.get('publication_year', '?')})")
print(f" Authors: {', '.join(authors)}")
print(f" Citations: {paper.get('cited_by_count', 0)}")
get_citing_works("W2741809807")
def find_related(openalex_id, limit=10):
"""Find papers related to a given paper via OpenAlex."""
# Get the paper's concepts, then search for similar works
resp = requests.get(
f"{OA_API}/works/{openalex_id}",
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"},
)
paper = resp.json()
concepts = [c["display_name"] for c in paper.get("concepts", [])[:3]]
related_resp = requests.get(
f"{OA_API}/works",
params={
"search": " ".join(concepts),
"per_page": limit,
"sort": "cited_by_count:desc",
},
headers={"User-Agent": "ResearchPlugins/1.0 (https://wentor.ai)"},
)
return related_resp.json().get("results", [])
related = find_related("W2741809807")
for p in related:
print(f"[{p.get('publication_year')}] {p.get('title')} ({p.get('cited_by_count')} cites)")
### LaTeX Workflow
1. Search: "Find papers on transformer efficiency"
2. Select relevant papers from results
3. Generate BibTeX entries → append to references.bib
4. Insert \cite{key} in your .tex file
### Markdown Workflow
1. Search for papers while writing
2. Get formatted citation (APA, MLA, etc.)
3. Insert inline: (Author, Year) or [1]
4. Generate reference list at document end
def build_bibliography(queries, output_file="refs.bib"):
"""Build BibTeX file from multiple search queries."""
all_bibtex = []
seen_ids = set()
for query in queries:
papers = search_papers(query, limit=3)
for paper in papers:
doi = paper.get("doi")
if doi and doi not in seen_ids:
seen_ids.add(doi)
bibtex = get_bibtex(doi.replace("https://doi.org/", ""))
all_bibtex.append(bibtex)
with open(output_file, "w") as f:
f.write("\n\n".join(all_bibtex))
print(f"Wrote {len(all_bibtex)} entries to {output_file}")
build_bibliography([
"attention mechanism",
"transformer architecture",
"BERT pre-training",
])