Paper Lookup

This skill gives you 18 scholarly APIs with documented endpoints. Your job is to turn the user's intent into a reproducible retrieval: pick the authoritative database(s), make bounded and rate-limited calls, and return an answer with enough provenance (endpoints, parameters, identifiers, access date) that a human or another agent can repeat it.

A literature lookup is only as trustworthy as it is repeatable. Prefer explicit identifiers and documented endpoints over broad guessing, report what you queried, and say plainly when a result is partial or a database came back empty — a silent gap reads as "nothing exists" when it may just mean "not indexed here."

Endpoint contracts were reviewed against official documentation on 2026-09-30. Response examples are illustrative shapes or explicitly dated historical captures; counts, prices, availability, and status values are not current lookup results. Selected public smoke calls and offline parser tests do not establish authenticated full-text access.

These APIs can fail with HTTP 200. That is the recurring hazard, and the reason for most of the rules below. PMC eFetch returns a well-formed article with no <body> when the publisher forbids redistribution. arXiv returns totalResults: 1 and one entry titled Error for a malformed parameter, and silently rewrites an unknown field prefix to all:. Europe PMC puts errCode in a 200 body. bioRxiv treats cursors as absolute offsets, so an incorrect step skips records. Figshare GET /articles?search_for= ignores the query and still 200s. OpenCitations answers an unknown DOI with [{"count": "0"}]. None of these raise, and every one of them produces a confident, wrong answer. Verify the shape of what you got, not just the status code.

Core Workflow

1. Define the retrieval contract — What is the user after? A specific paper by DOI/PMID/arXiv ID? Papers on a topic? An author's publications? A citation graph? An open-access PDF? Full text? Note any constraints that change the answer: date range, field of study, open-access-only, exhaustive list vs. a few top hits. If a constraint that affects correctness is missing (e.g., "recent" with no year, or an author name with many namesakes), ask rather than guess.

2. Select database(s) — Use the selection guide below. Route to the primary database for the intent, then add others only when they earn their place: identifier resolution, open-access lookup, or a known coverage gap. Don't fan out across all eighteen just because they're available.

3. Read the reference file — Each database has a file in references/ with endpoints, parameters, example calls, response shapes, and the specific ways it fails quietly. Read the relevant file(s) before calling. The hazard sections are not optional background; they are where the wrong answers come from.

4. Prefer the bundled scripts over hand-rolled parsing — See Bundled Scripts. Pagination, JATS full text, arXiv Atom, and OpenAlex abstracts each have a script that already handles the traps. Reaching for python3 -c instead is how the traps get re-introduced.