OpenAlex
Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.
Install / Use
npx skills add xuzhougeng/wisp-science --skill literature-reviewInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
CommunicationSupported Platforms
Our assessment of OpenAlex
OpenAlex scores 83/100 on our quality scale, 328th of 421 Communication skills we index.
Its SKILL.md is 6.6 KB long, split into 7 sections and no code examples: a thorough specification that gives an agent plenty to work with.
With 1,167 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 8 days ago, so OpenAlex is actively maintained.
- It is released under AGPL-3.0, a copyleft license: you can use it, but modified versions you distribute must carry the same license.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-02. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
OpenAlex compared with similar skills
All 4 of these similar skills score higher than OpenAlex; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| OpenAlex (this skill)by xuzhougeng | 83 | 1.2k | 8d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 88.6k | 17d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.2k | 2d ago | MCP Server |
| crawl4aiby unclecode | 100 | 84.6k | 7d ago | MCP Server |
Frequently asked questions
- How do I install OpenAlex?
- Run
npx skills add xuzhougeng/wisp-science --skill OpenAlex. The install tabs above show the steps for each supported agent. - Which AI agents does OpenAlex work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is OpenAlex safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is AGPL-3.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is OpenAlex still maintained?
- The repository was last updated 8 days ago, so OpenAlex is actively maintained.
Skill content
View source on GitHubname: literature-review description: Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links. license: Apache-2.0 metadata:
Non-biomodel: sends user's query (and contact email when configured) to
Crossref and OpenAlex for literature lookup.
third_party: # The leaf /rest-api-metadata-license-information/ page now 404s though # still in search indexes. Parent docs landing carries the license # statement ("Almost all of the metadata we hold is reusable without # restriction") and is less likely to rot. Docs page, not a ToU — # info_url. verified 2026-06-30 - kind: service name: Crossref info_url: https://www.crossref.org/documentation/retrieve-metadata/ privacy_url: https://www.crossref.org/operations-and-sustainability/privacy/ - kind: service name: OpenAlex terms_url: https://openalex.org/OpenAlex_termsofservice.pdf privacy_url: https://openalex.org/OpenAlex_privacy_policy.pdf wisp: schema_version: 1 domains: [scientific-literature] research_stages: [retrieval, validation, synthesis] roles: [retrieval, critic, synthesizer] evidence_types: [literature] outputs: [literature-review, evidence-matrix] side_effects: network
Literature review
Work through six steps: scope, sweep, expand, verify, write, lint. The failure modes this skill exists to prevent are all silent — a fabricated DOI, a retracted headline result, a reading list dressed up as a synthesis — so each step below names the check that catches it.
1. Scope the request
Different phrasings want different deliverables:
| Request shape | Deliverable | |---|---| | "the paper for X" / "the original/seminal…" | one or two primary citations | | "what's the evidence on X" | thematic synthesis | | "compare A and B" | trade-off analysis ending in a recommendation | | "where are the gaps" | named gaps, each anchored to what establishes it |
A vague lay query gets the scope a domain expert would default to, stated explicitly ("taking this as human RCT evidence; animal work is separate"). Clarify with the user only when the answer would change what you retrieve.
2. Sweep
Never write from recall. Recall chooses the framing and the search terms;
retrieval supplies every citation. Start with search_openalex /
crossref_lookup from this skill's runtime.py, a PubMed query, or any
literature connector advertised in the session (search_skills with
{"query":"literature PubMed Semantic Scholar bioRxiv ClinicalTrials"} finds
installed guidance; load matches with use_skill).
For a named-paper lookup, the target is the highly cited primary publication that later work cites — not a review of it, not a news piece. Even when you know the paper cold, resolving its DOI is one tool call; skipping it turns a citation into a claim about a citation.
3. Expand along the citation graph
Keyword sweeps miss two things systematically: the foundational paper a field
builds on, and the newest work that extends or contests your top hits. Take
the two or three most relevant results and run expand_citations(doi) — it
returns references (backward) and cited-by (forward) from OpenAlex. Fold the
on-topic finds back into the working set before drafting. A survey-grade
answer typically rests on fifteen or more distinct primary-paper DOIs; a
handful of reviews is a reading list.
The Python OpenAlex helpers raise on HTTP errors, timeouts, or malformed responses. Empty results are valid only after successful retrieval. If either citation direction fails, report the retrieval failure rather than treating the partial graph as complete. Do not convert an exception into an empty list.
4. Verify
Run verify_dois on everything you intend to cite. Distinguish registered,
not resolving, and unverified (ok=None, e.g. network failure) results. A
registered DOI still requires reading the paper to check whether it supports
the claim; a failed request is not evidence of fabrication. When you have
author/year/journal but no DOI, look it up; never
pattern-complete one. For surprising or high-profile findings, check
Crossref's update-to field: sensational papers are findable because they
were sensational, and some were retracted. When the requested paper does not
exist — the claim collapsed or was never established — say exactly that and
point at what the evidence actually shows, instead of substituting the
nearest-matching citation.
5. Write the synthesis
Organize by question or theme, never paper-by-paper. The value is the layer on top of the papers: what replicated, what didn't, where the field agrees on effect but splits on mechanism, which older result a newer one superseded. Two tests for the draft:
- First-sentence test. Read only each paragraph's opening sentence. In sequence they should form your argument; if they form a list of author names, you have an annotated bibliography.
- Bullet test. Consecutive lines starting
- Author Year showed…are a paragraph you haven't written. Bullets are for genuinely enumerable things (a reference appendix, a comparison table); the argument itself is prose.
Calibrate stated confidence to the evidence: a phase-3 RCT is stated plainly, a single-cohort finding is "one group reported", preprints are flagged as preprints, contested areas get both sides plus an honest "unresolved". Engage a contested premise rather than building on it.
Cite inline as [Author Year](https://doi.org/10.xxxx/...) so prose renders
as (Author Year) with the DOI in the href. URL-encode parentheses inside a
DOI as %28/%29. No numbered [1] references — they desync on reorder.
Headings are short noun phrases; with five or more topics, group under two or
three ## and demote the rest to ###.
6. Deliver and lint
The answer lives in the chat reply: open on the finding itself, lay out the evidence with inline DOIs, close on what remains open. For anything beyond a one-paper lookup, also save the full review to a project-relative Markdown file and link it at the end of the reply. Process narration — "all DOIs verified", "no retraction flags", "report saved" — belongs nowhere: not as opener, footer, or subtitle. Verification lives in the tool trace.
Before saving, run style_pass(draft) from runtime.py once on the full
markdown, fix what it lists in one editing pass, and save. It is a lint, not
a gate — do not loop on it. If style_pass is not defined in the kernel,
read this skill's runtime.py and exec it first.
Related Skills
Agent-Reach
88.6kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
74.3kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
Scrapling
85.2k🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! Don't be shy, join here: https://discord.gg/EMgGbDceNQ and follow here for daily tips and tricks: https://x.com/Scrapling_dev
crawl4ai
84.6kOpen-source web crawler and scraper for LLMs and AI agents: any website into clean, LLM-ready Markdown. Run it yourself, or use Crawl4AI Cloud with one key.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
