literature-search-openalex▌
google-deepmind/science-skills · updated Jun 4, 2026
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### Literature Search Openalex
- ›name: "literature-search-openalex"
- ›description: "Query the OpenAlex scholarly database for research papers, authors, institutions, topics, sources, publishers, funders, geo-locations, and keywords. Use when searching academic papers, resolving DOIs,..."
| name | literature-search-openalex |
| description | > Query the OpenAlex scholarly database for research papers, authors, institutions, topics, sources, publishers, funders, geo-locations, and keywords. Use when searching academic papers, resolving DOIs, downloading open-access PDFs, finding an author's publications, aggregating bibliometric data (citation counts, h-index, impact factor), exploring the research taxonomies, or performing DOI lookups. |
OpenAlex Skill
Prerequisites
-
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH. -
User Notification: If LICENSE_NOTIFICATION.txt does not already exist in this skill directory then (1) prominently notify the user to check the terms at https://developers.openalex.org/ and to always check the license of the papers retrieved by the skill for any restrictions, then (2) create the file recording the notification text and timestamp.
-
.envfile: Make sure the.envfile exists in your home directory. Create one if it does not exist. -
OPENALEX_API_KEY(optional but recommended): Enables the OpenAlex Premium API with higher rate limits. The skill works without it (using the free "polite pool"). If the variable is missing from.env, do NOT ask the user to paste it into the chat (this would leak the key into the agent's context). Instead, give the user this command — substitutingENV_FILEwith the resolved literal path to the.envfile:printf "Enter OpenAlex API key (typing hidden): " && read -s key && echo && echo "OPENALEX_API_KEY=$key" >> "ENV_FILE" && echo "Saved."The scripts load credentials automatically via
dotenv. NEVER read, print, or inspect the.envfile or its variables (e.g. nocat,grep,echo,printenv, oros.environ.geton keys). Credentials must stay out of the agent's context. See the Rate Limits section for more details.
Core Rules
- List Sources. If this skill is used, ensure this is mentioned in the output AND list the URLs of all papers that were used in producing the output.
- Resolve before filter. NEVER filter by name. Always
resolvea name to an ID first, then use that ID in--filter. - Use the CLI only. Never call the API via
curl/urllib. The CLI handles retries and rate limiting. - No fabrication. Never invent OpenAlex IDs or DOIs. Use
resolve/getto look them up. Report empty results accurately. - API key. If a command returns 401/429 or you need high-volume queries,
follow the prerequisite instructions above to help the user add
OPENALEX_API_KEYto the.envfile. Keys are at OpenAlex.org → account settings. - Keep output small. Always use
--selectand--per-page 5–10for overview queries. Pipefilteroutput to a file (> results.json), then slim withjqbefore reading into context.
Rate Limits
- With key: ~10 req/s, $1/day free budget.
- Without key: Very limited, $0.01/day budget.
| Operation | Cost |
|---|---|
Singleton get | Free |
filter | $0.0001 |
--search / resolve | $0.001 |
download-pdf | $0.01 |
CLI Reference
uv run scripts/openalex_cli.py [--api-key KEY] <command> [flags]
Entity types (shared across commands): works, authors, sources,
institutions, topics, domains, fields, subfields, sdgs, countries,
continents, languages, keywords, publishers, funders, work-types,
source-types, institution-types, licenses
Commands
resolve <entity> <query> — Name → ID candidates. Returns id,
display_name, hint. Use --per-page N for more candidates.
get <entity> <id> — Full metadata for one entity. Accepts short ID
(W2741809807), full URL, or DOI URL. Use --select to limit fields.
filter <entity> — Search/filter entities. Key flags are:
--search <query>: Full-text search (10× cost of--filter)--filter <expr>: Filter expressions. Use,for AND and|for OR.--sort <field:dir>: Sort results (e.g.,cited_by_count:desc)--select <fields>: Limit the fields returned in the output.--group-by <field>: Aggregate results by a specific field.--per-page <N>: Number of results per page (default 25, max 100).--page <N>: Specify the page number to retrieve.--sample <N>: Get a random sample of up to 10,000 results.--seed <N>: Seed for reproducible sampling.
download-pdf <work-id> <output-path> — Download PDF (requires API key).
Falls back to alternative pdf_url locations if primary fails. Whenever you
download a PDF, verify it is not empty or corrupted.
rate-limit — Check current rate limit status (requires API key).
Search Tips
- If
resolvereturns no matches, try alternate spellings or abbreviations. - If
--searchreturns 0 results, try broader terms (max 3 retries). - If
resolvereturns multiple candidates, present them to the user withdisplay_nameandhintfor manual selection.
Entity References
Consult references/ for valid filter, sort, and group-by fields per entity:
- Works — Authors — Sources
- Institutions — Topics — Taxonomy
- Geo & Language — Publishers & Funders
- Type Values
Common Workflows
# Author's works (resolve → filter)
uv run scripts/openalex_cli.py resolve authors "Geoffrey Hinton"
uv run scripts/openalex_cli.py filter works \
--filter "authorships.author.id:A5108093963" \
--sort "cited_by_count:desc" --per-page 10 > papers.json
cat papers.json | jq '[.results[] | {id, title: .display_name, year: .publication_year, citations: .cited_by_count}]'
# DOI lookup
uv run scripts/openalex_cli.py get works "https://doi.org/10.1038/s41586-021-03819-2"
# Bulk DOI lookup (up to 100)
uv run scripts/openalex_cli.py filter works \
--filter "doi:10.1234/a|10.1234/b|10.1234/c" --per-page 100 > results.json
# Institutional impact by year
uv run scripts/openalex_cli.py resolve institutions "MIT"
uv run scripts/openalex_cli.py filter works \
--filter "authorships.institutions.id:I63966007" \
--group-by "publication_year" > mit_by_year.json
# Random sample
uv run scripts/openalex_cli.py filter works \
--filter "publication_year:2023,is_oa:true" \
--sample 100 --seed 42 > results.json
Error Handling
| Code | Meaning | Action |
|---|---|---|
| 401 | Unauthorized | Help user add API key to .env (see prereqs) |
| 403 | Plan upgrade needed | Inform user; see https://openalex.org/pricing |
| 404 | Not found | Verify ID; try resolve first |
| 429 | Rate limited | Wait and retry; suggest adding API key to .env |
Known premium-only filters: from_updated_date, to_updated_date.
Never fabricate results on empty responses — report accurately and suggest alternate search terms.
How to use literature-search-openalex on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add literature-search-openalex
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches literature-search-openalex from GitHub repository google-deepmind/science-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate literature-search-openalex. Access the skill through slash commands (e.g., /literature-search-openalex) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★49 reviews- ★★★★★Nikhil Dixit· Dec 24, 2024
Keeps context tight: literature-search-openalex is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Noor Abebe· Dec 20, 2024
literature-search-openalex has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aanya Diallo· Dec 4, 2024
I recommend literature-search-openalex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aanya Huang· Nov 23, 2024
Useful defaults in literature-search-openalex — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Li Agarwal· Nov 19, 2024
literature-search-openalex is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ren Chen· Nov 15, 2024
literature-search-openalex has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Lucas Taylor· Nov 11, 2024
Keeps context tight: literature-search-openalex is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chen Zhang· Oct 14, 2024
Registry listing for literature-search-openalex matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Lucas Haddad· Oct 10, 2024
literature-search-openalex reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Aarav Anderson· Oct 6, 2024
literature-search-openalex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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