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literature-search-openalex

google-deepmind/science-skills · updated Jun 4, 2026

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$npx skills add https://github.com/google-deepmind/science-skills --skill literature-search-openalex
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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,..."
skill.md
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

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.

  2. 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.

  3. .env file: Make sure the .env file exists in your home directory. Create one if it does not exist.

  4. 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.

Core Rules

  1. 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.
  2. Resolve before filter. NEVER filter by name. Always resolve a name to an ID first, then use that ID in --filter.
  3. Use the CLI only. Never call the API via curl/urllib. The CLI handles retries and rate limiting.
  4. No fabrication. Never invent OpenAlex IDs or DOIs. Use resolve/get to look them up. Report empty results accurately.
  5. 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_KEY to the .env file. Keys are at OpenAlex.org → account settings.
  6. Keep output small. Always use --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.

Rate Limits

  • With key: ~10 req/s, $1/day free budget.
  • Without key: Very limited, $0.01/day budget.
OperationCost
Singleton getFree
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 resolve returns no matches, try alternate spellings or abbreviations.
  • If --search returns 0 results, try broader terms (max 3 retries).
  • If resolve returns multiple candidates, present them to the user with display_name and hint for manual selection.

Entity References

Consult references/ for valid filter, sort, and group-by fields per entity:

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

CodeMeaningAction
401UnauthorizedHelp user add API key to .env (see prereqs)
403Plan upgrade neededInform user; see https://openalex.org/pricing
404Not foundVerify ID; try resolve first
429Rate limitedWait 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

How to use literature-search-openalex on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/google-deepmind/science-skills --skill literature-search-openalex

The skills CLI fetches literature-search-openalex from GitHub repository google-deepmind/science-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/literature-search-openalex

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

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.749 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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