literature-review

Literature review

A literature question has two halves: finding the papers a domain expert would point to, and turning them into something more useful than a reading list — a synthesis that says what's established, what's contested, what's new, and where the holes are. Both halves can fail quietly and look like competent output until someone checks.

Setup (any agent, no API key)

This is a pure skill — kernel.py is deterministic Python (plain HTTP/stdlib calls to CrossRef and OpenAlex) and you (the base model) do all the reasoning: the finding, the synthesis, the prose. There is no host runtime and no LLM API. Load the helpers once per session in a Python cell:

exec(open("<this skill's directory>/kernel.py").read())

Nothing auto-loads it outside Claude Science. Then call the helpers directly — verify_dois, crossref_lookup, search_openalex, expand_citations, extract_dois, style_pass. If a helper name is not defined, you haven't exec'd kernel.py.

Configuration is via environment variables, not a host — no LLM key is involved:

• OPENALEX_API_KEY — required for the OpenAlex-backed steps (search_openalex, expand_citations); free at https://openalex.org/settings/api.
• HOST_USER_EMAIL — optional contact email for the CrossRef/doi.org polite pool (falls back to git config user.email; never sent to OpenAlex).

Read the request for what it's actually asking

"What's the paper for X" wants one or two specific citations; "what's the evidence on X" wants a synthesis; "compare A and B" wants a comparison, not two adjacent summaries; "where are the gaps" wants the gaps, with the survey as supporting material. A two-word lay query wants you to choose the scope a domain expert would default to and say so up front — "I'll take this as asking about human RCT evidence; the animal literature is separate." Ask a clarifier only when the answer would genuinely change what you do.

Grounding: retrieve first, then write

For broad-survey, where-are-the-gaps, and compare-methods requests, the first move is a literature sweep — search_openalex / crossref_lookup from kernel.py, plus your agent's own web search and any literature/data MCP tools it has connected (PubMed, Semantic Scholar, bioRxiv, ClinicalTrials.gov, …), using whichever fits the field — and the answer is built from what comes back. Your recall picks the framing; the retrieval picks the citations. A real survey usually carries on the order of fifteen or more distinct primary-paper DOIs, because each claim is anchored to the paper that established it; a handful of review citations is a reading list, not a synthesis. When the question is after a specific paper — "the original," "the seminal," a named trial or method — find the highly-cited primary publication that the follow-ups all cite, not a review or news piece about it.