Search academic papers by topic, year, citation count, and free availability. Organize abstracts and publication details into a comparison table, and investigate author affiliations, funding sources, and citation links through OpenAlex.
中文简介正在准备中。当前显示英语简介。
Search academic papers via OpenAlex API for open citation data, institutional affiliations, and funding information. Use when user says "openalex search", "search openalex", "open citation graph", or wants comprehensive academic metadata beyond arXiv/Semantic Scholar.
安装前可以查看交给 AI 代理的指令内容。
Search query: $ARGUMENTS
This skill uses OpenAlex as a comprehensive open academic graph source:
| Skill | Source | Best for |
|---|---|---|
/arxiv | arXiv API | Latest preprints, cutting-edge unrefereed work |
/semantic-scholar | Semantic Scholar API | Published venue papers (IEEE, ACM, Springer) with citation counts |
/openalex | OpenAlex API | Open citation graph, institutional affiliations, funding data, comprehensive metadata |
/deepxiv | DeepXiv CLI | Layered reading: search, brief, section map, section reads |
/exa-search | Exa API | Broad web search: blogs, docs, news, companies, research papers |
/gemini-search | Gemini MCP / CLI | AI-powered broad literature discovery |
Use OpenAlex when you want:
— max: 20.— sort: citations or — sort: date.openalex_fetch.py, resolved per
shared-references/integration-contract.md §2
(Policy D1 — standalone /openalex has no documented inline fallback,
so unresolved helper terminates with an explicit error).Overrides (append to arguments):
/openalex "topic" — max: 20— return up to 20 results/openalex "topic" — year: 2023-— papers from 2023 onward/openalex "topic" — year: 2020-2023— papers from 2020 to 2023/openalex "topic" — type: article— only journal articles/openalex "topic" — type: preprint— only preprints/openalex "topic" — open-access— only open access papers/openalex "topic" — min-citations: 50— minimum 50 citations/openalex "topic" — sort: citations— sort by citation count (descending)/openalex "topic" — sort: date— sort by publication date (newest first)
Python 3.7+ with requests library:
pip install requests
Optional: API keys — Create .claude/.env in project root:
# Copy from template
cp .claude/.env.example .claude/.env
# Edit and add your keys
# .claude/.env
OPENALEX_API_KEY=your-key-here
OPENALEX_EMAIL=your-email@example.com
Claude Code automatically loads .claude/.env as environment variables.
Get API keys (optional but recommended):
python3 "$OPENALEX_FETCHER" search "machine learning" --max 3
(Resolve $OPENALEX_FETCHER via the canonical chain first — see Step 2 below.)
Parse $ARGUMENTS for:
2023-, 2020-2023)article, preprint, book, book-chapter, dataset, dissertation)relevance, citations, date)Resolve $OPENALEX_FETCHER via the canonical strict-safe chain (see
shared-references/integration-contract.md §2).
Policy D1: there is no native inline fallback for OpenAlex
(retrieval requires the requests SDK + optional API key — the
fetcher script encapsulates pagination, throttling, and per-source
parameters), so unresolved helper terminates with explicit remediation.
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
OPENALEX_FETCHER=".aris/tools/openalex_fetch.py"
[ -f "$OPENALEX_FETCHER" ] || OPENALEX_FETCHER="tools/openalex_fetch.py"
[ -f "$OPENALEX_FETCHER" ] || { [ -n "${ARIS_REPO:-}" ] && OPENALEX_FETCHER="$ARIS_REPO/tools/openalex_fetch.py"; }
[ -f "$OPENALEX_FETCHER" ] || {
echo "ERROR: openalex_fetch.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
echo " Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2
echo " Also ensure 'requests' is installed: pip install requests" >&2
exit 1
}
Basic search:
python3 "$OPENALEX_FETCHER" search "QUERY" --max 10
With filters:
python3 "$OPENALEX_FETCHER" search "QUERY" --max 10 \
--year 2023- \
--type article \
--open-access \
--min-citations 20 \
--sort citations
Get specific work by DOI:
python3 "$OPENALEX_FETCHER" work "10.1109/TWC.2024.1234567"
Get specific work by OpenAlex ID:
python3 "$OPENALEX_FETCHER" work "W2741809807"
The script returns structured JSON with:
title: Paper titleauthors: List of author namespublication_year: Year publishedvenue: Journal/conference namevenue_type: Type of venue (journal, repository, conference, etc.)cited_by_count: Number of citationsis_oa: Boolean for open access statusoa_status: Open access type (gold, green, bronze, hybrid, closed)oa_url: Direct PDF link if availabledoi: DOI identifieropenalex_id: OpenAlex work IDabstract: Full abstract texttopics: Top 3 research topicskeywords: Top 5 keywordstype: Work type (article, preprint, etc.)Format results as a structured table:
| # | Title | Venue | Year | Citations | OA | Summary |
|---|-------|-------|------|-----------|----|---------|
| 1 | ... | IEEE TWC | 2024 | 156 | ✓ | ... |
| 2 | ... | NeurIPS | 2023 | 89 | ✓ | ... |
For each paper, also show:
After presenting results, suggest:
/semantic-scholar "DOI:..." — get S2 citation context and related papers
/arxiv "arXiv:XXXX.XXXXX" — fetch arXiv preprint if available
/research-lit "topic" — sources: openalex, semantic-scholar — combined multi-source review
/novelty-check "idea" — verify novelty against literature
OPENALEX_EMAIL environment variable for faster response times/semantic-scholar, /arxiv, or /research-lit "topic" — sources: web as alternatives.| Feature | OpenAlex | Semantic Scholar | arXiv |
|---|---|---|---|
| Coverage | 250M+ works | 200M+ papers | 2.4M+ preprints |
| Citation data | Fully open | Partially open | None |
| Institutions | ✓ Full affiliations | ✓ Limited | ✗ |
| Funding | ✓ NSF, NIH, etc. | ✗ | ✗ |
| Open access | ✓ Full OA status | ✓ PDF links | ✓ All papers |
| API key | Optional (free) | Optional (free) | Not required |
| Rate limits | 1,000 searches/day (free key) | Unknown | 1 req/3s |
| Abstract | ✓ Full text | ✓ TLDR | ✓ Full text |
| Best for | Comprehensive metadata, institutions, funding | Citation counts, venue info | Latest preprints |
When to use OpenAlex over S2:
When to use S2 over OpenAlex:
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Plan and run ablation studies, experiments that remove or replace parts of a method to test their contribution. Organize reviewer questions, priorities, expected outcomes, and compute costs, then record results and findings.
中文简介正在准备中。当前显示英语简介。
Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.
中文简介正在准备中。当前显示英语简介。
Understand a single arXiv paper through an AlphaXiv summary, with full text and LaTeX source available when needed. Use it to identify key findings or ask specific questions about methods, experiments, and equations.
中文简介正在准备中。当前显示英语简介。
Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.
中文简介正在准备中。当前显示英语简介。
Search arXiv papers by topic or paper ID, review authors and abstracts, and summarize contributions stated in the abstracts. Download selected papers or all search results as PDFs to build a local paper library.
中文简介正在准备中。当前显示英语简介。
Improve a compiled LaTeX paper through two rounds of Claude review, edits, and recompilation. The workflow addresses inconsistent reasoning, unsupported claims, unclear writing, and visual presentation, while keeping a cumulative log of changes.
中文简介正在准备中。当前显示英语简介。