菜单
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
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。
基于 SOC 职业分类
| name | literature-search |
| description | Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography. |
| argument-hint | ["search-query"] |
Search multiple academic databases to find relevant papers.
$ARGUMENTS — The search query (natural language)python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py \
--query "QUERY" --max-results 20 --year-range 2022-2026 \
--api-key "$(grep S2_API_Key /Users/lingzhi/Code/keys.md 2>/dev/null | cut -d: -f2 | tr -d ' ')" \
-o results_s2.jsonl
Key flags: --peer-reviewed-only, --top-conferences, --min-citations N, --venue NeurIPS ICML
python ~/.claude/skills/deep-research/scripts/search_arxiv.py \
--query "QUERY" --max-results 10 -o results_arxiv.jsonl
python ~/.claude/skills/literature-search/scripts/search_openalex.py \
--query "QUERY" --max-results 20 --year-range 2022-2026 \
--min-citations 5 -o results_openalex.jsonl
python ~/.claude/skills/deep-research/scripts/paper_db.py merge \
--inputs results_s2.jsonl results_arxiv.jsonl results_openalex.jsonl \
--output merged.jsonl
python ~/.claude/skills/literature-search/scripts/search_crossref.py \
--query "QUERY" --rows 10 --output results_crossref.jsonl
Key flags: --bibtex (output .bib format), --rows N
python ~/.claude/skills/literature-search/scripts/download_arxiv_source.py \
--title "Paper Title" --output-dir arxiv_papers/
Key flags: --arxiv-id 1706.03762, --metadata, --max-results N
python ~/.claude/skills/deep-research/scripts/bibtex_manager.py \
--jsonl merged.jsonl --output references.bib
Tier 1: NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV, ECCV, KDD, AAAI, IJCAI, SIGIR, WWW
Tier 2: AISTATS, UAI, COLT, COLING, EACL, WACV, JMLR, TACL
Tier 3: Workshops, arXiv preprints — mark with (preprint)
Present results as a table + detailed entries with BibTeX keys. Always note preprint status.
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
Explore and analyze GitHub repositories related to a research topic. Reads deep-research output, discovers repos from multiple sources, deeply analyzes code, and produces integration blueprints.
Manage BibTeX citations for LaTeX papers. Harvest missing citations from a draft using Semantic Scholar, validate cite keys against .bib files, deduplicate entries, and format bibliography. Use when working with references, BibTeX, or citations.
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Write ML experiment code with iterative improvement. Generate training/evaluation pipelines, debug errors, and optimize results through code reflection. Use when implementing experiments for a research paper.
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.