--- 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 Skill Guide ## Overview 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. ## Installation ```bash # Add as Claude Code skill # Copy SKILL.md to your Claude Code skills directory # Or install via OpenClaw: openclaw skills install citation-assistant ``` ## Core Features ### Paper Search ```python 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')}") ``` ### BibTeX Generation ```python 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", [""])[0] if msg.get("container-title") else "" bibtex = f"""@article{{{key}, title = {{{title}}}, author = {{{authors_str}}}, year = {{{year}}}, journal = {{{journal}}}, doi = {{{doi_str}}}, }}""" return bibtex # Example bibtex = get_bibtex("10.18653/v1/N19-1423") print(bibtex) ``` ### Citation Context ```python 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") ``` ### Related Paper Discovery ```python 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)") ``` ## Workflow Integration ```markdown ### 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 ``` ## Batch Operations ```python 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", ]) ``` ## Use Cases 1. **Paper writing**: Look up and format citations inline 2. **Literature review**: Discover related papers from seed papers 3. **Reference management**: Build BibTeX files from searches 4. **Citation analysis**: Explore how papers cite each other 5. **Quick lookup**: Find DOI, venue, citation count for any paper ## References - [Citation Assistant GitHub](https://github.com/ZhangNy301/citation-assistant) - [OpenAlex API](https://api.openalex.org/) - [CrossRef API](https://api.crossref.org/) --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/writing/citation/citation-assistant-skill/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/writing/citation/citation-assistant-skill/SKILL.md Attribution basis: Source repository record Fields of work: Education & Research, Marketing Roles: Academic Writer, Content Marketer, Researcher Specialties: API Tools: OpenAI API, Crossref, Openalex Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: cd07263fea2296168ac92c039f523848357192f47586dcded0fc02d0b0b1ecb4