### Literature Search Openalex
Works with
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,..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionliterature-search-openalexExecute the skills CLI command in your project's root directory to begin installation:
Fetches literature-search-openalex from google-deepmind/science-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate literature-search-openalex. Access via /literature-search-openalex in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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| 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. |
uv: Read the uv skill and follow its Setup instructions to ensure
uv is 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.
.env file: Make sure the .env file 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 — substituting ENV_FILE
with the resolved literal path to the .env file:
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 .env file or its variables (e.g. no cat, grep,
echo, printenv, or os.environ.get on keys). Credentials must stay out
of the agent's context. See the Rate Limits section for more
details.
resolve a name to
an ID first, then use that ID in --filter.curl/urllib. The CLI
handles retries and rate limiting.resolve/get
to look them up. Report empty results accurately.OPENALEX_API_KEY to the .env file. Keys are at OpenAlex.org → account
settings.--select and --per-page 5–10 for
overview queries. Pipe filter output to a file (> results.json), then
slim with jq before reading into context.| Operation | Cost |
|---|---|
Singleton get | Free |
filter | $0.0001 |
--search / resolve | $0.001 |
download-pdf | $0.01 |
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
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).
resolve returns no matches, try alternate spellings or abbreviations.--search returns 0 results, try broader terms (max 3 retries).resolve returns multiple candidates, present them to the user with
display_name and hint for manual selection.Consult references/ for valid filter, sort, and group-by fields per entity:
# 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
| 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.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
google-deepmind/science-skills
google-deepmind/science-skills
google-deepmind/science-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
Keeps context tight: literature-search-openalex is the kind of skill you can hand to a new teammate without a long onboarding doc.
literature-search-openalex has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend literature-search-openalex for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in literature-search-openalex — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
literature-search-openalex is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
literature-search-openalex has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: literature-search-openalex is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for literature-search-openalex matched our evaluation — installs cleanly and behaves as described in the markdown.
literature-search-openalex reduced setup friction for our internal harness; good balance of opinion and flexibility.
literature-search-openalex fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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