/litreview — Multi-source literature search and synthesis

The goal is to give the user a high-signal, ranked, deduped paper list with enough takeaway per item that they can decide which to read, which to cite, and which to skip — all in one pass. The output is the input to /draft (when writing a lit review) and to /cite (when batch-adding the best hits to Zotero).

Modes

• Default (no flag): single 1–5 relevance score per paper — exactly as documented in step 3 below. This is the canonical mode.
• `--four-axis` (opt-in): replace the single score with four 1–5 sub-scores — Novelty (vs. the existing lit on this topic), Credibility (venue tier + author rep + sample / data quality), Relevance (fit to the specific project context), Actionability (cite-as-prior / replicate-method / pivot-target / superseded). Composite = arithmetic mean of the four, rounded to one decimal. The breakdown is rendered only when composite >= 4 (else show only the composite, to keep output tight). All downstream thresholds — ranking, in-Zotero check, the /cite handoff — use the composite as a drop-in replacement for the single score, so the composite >= 4 cutoff matches the default score >= 4 cutoff exactly. Default behavior is unchanged unless --four-axis appears in the invocation.

When to invoke

The user invokes /litreview <query> with a topic, paper, or research question. Common shapes:

• /litreview discrimination in online labor markets using AI-generated profile images
• /litreview sparse autoencoder steering for text generation
• /litreview meta-science applied to social media engagement prediction
• /litreview cite-by-DOI 10.1287/mksc.2023.1234 (anchor mode — seed paper)
• /litreview "moral outrage" Brady PNAS replications
• /litreview generative AI in product design since 2022 open access n=30

Loosely-stated flags inside the query — interpret them, don't require a CLI:

Required MCP tools (verified live names)

Run these in parallel for source coverage. If a server is down, continue with whichever responded.

• mcp__arxiv__search_papers — primary for recent ML/CS/preprints.
• mcp__arxiv__semantic_search — embedding-similarity within a local arXiv corpus (if pre-indexed). Useful for "papers similar to <seed arXiv ID>".
• mcp__arxiv__get_abstract — fetch abstract by ID after a hit.
• mcp__semantic-scholar__search_papers — primary for published / peer-reviewed work and citation counts.
• mcp__semantic-scholar__get_paper_details — full metadata + DOI + venue + OA link.
• mcp__semantic-scholar__get_paper_references / mcp__semantic-scholar__get_paper_citations — for seed-paper expansion ("papers like X", "papers citing Y").
• mcp__semantic-scholar__get_recommendations — Semantic Scholar's recommendation engine.
• mcp__openalex__openalex_search_entities — broadest interdisciplinary venue coverage, best for social-science / marketing / policy work that Semantic Scholar misses.
• mcp__openalex__openalex_analyze_trends — only when the user asks "how has the literature evolved" or similar trend question.

Fallback (manual) when all three MCPs miss something specifically expected: Crossref REST API via Invoke-WebRequest (https://api.crossref.org/works?query=<terms>&rows=20) — no auth, no MCP needed.

Out-of-loop tools: Zotero MCP (mcp__zotero__zotero_search_items) only for deduping against the existing library; the Zotero MCP is read-only — adds go through /cite, which uses the Zotero Web API directly.

Workflow

1. Plan the queries

Translate the natural-language query into 1–3 targeted search strings per source. Don't echo the exact phrase — strip filler ("can you find", "I'm interested in"), pull out the substantive nouns, expand acronyms (e.g. SAE → "sparse autoencoder"; but don't expand acronyms that are project shorthand for a topic — search the topic, not the acronym).

Tailor per source:

• arXiv: short keyword phrase, optional category filter (e.g., cs.LG, econ.EM, stat.ME).
• Semantic Scholar: full query string; use year>=2020 filter parameter if the user asked for recent work.
• OpenAlex: broader keyword set; include MeSH-like concept terms for biomedical/policy adjacency.

Run all three searches in parallel with limit=20 to 30 each so dedupe has headroom.

2. Dedupe

Build a unified candidate list. Dedupe in this order:

1. DOI exact match (strip https://doi.org/, lowercase).
2. arXiv ID exact match (handle both 2403.12345 and arXiv:2403.12345v2).
3. Title fuzzy match — lowercase, strip punctuation, tokenize; >=90% token-set overlap = same paper.
4. Author-first + year as a last-resort tiebreak.

When the same paper appears in multiple sources, merge metadata:

• Prefer Semantic Scholar for citation count, abstract, references.
• Prefer OpenAlex for venue name, concept tags, OA status.
• Prefer arXiv for the preprint version + latest version date.

3. Score relevance (1–5)

For each unique paper, score against the query using abstract + venue + year:

• 5 — directly on the question; central reference; would be cited in the intro of a paper on this topic.
• 4 — closely related; definitely cite; secondary support.
• 3 — adjacent; worth knowing about; might cite in a robustness or lit-review paragraph.
• 2 — tangential; mention only if asked.
• 1 — false positive; filter out before output.

Be honest. If only 8 papers score >=3, return 8 — not 25 padded with score-2 noise. Padding wastes the user's time and credibility.

4. Pre-existing-in-Zotero check (cheap dedupe against the library)

Before output, for the score >=4 papers, call mcp__zotero__zotero_search_items(query="<short title>", qmode="titleCreatorYear", limit=3) to check if it's already there. Flag as [in Zotero] in output — saves the /cite step on those.

5. Output — ranked summarized list

Order by score (descending), then year (newest within score). Format per paper:

**[1] (score=5, 2024) Athey, Imbens et al. — *Title in italics*. *Marketing Science*. doi:10.xxx
   Why relevant: <one sentence anchoring to the query>
   Key finding/contribution: <one short sentence>
   Method/identification: <one phrase — RDD, RCT, DiD, structural, observational, etc.>
   arXiv:24xx.xxxxx | OA: yes | citations: 142 | [in Zotero: existing-key | not yet]

After the list, give a 3–5 sentence synthesis through a quantitative-marketing / causal-inference lens:

• Which sub-themes dominate.
• Where the field disagrees (a real edge to position against).
• What's missing — gaps that could plausibly be filled or that the user's papers already address.

If the user asked for "add to zotero" or "send to /cite", proceed to step 6. Otherwise stop here and ask: "Add the score >=4 hits to Zotero via /cite?"

6. Optional: hand off top hits to /cite

For each paper with score >=4 (or whatever cap was given), pass DOI or arXiv ID to /cite in one batch. /cite handles:

• DOI/arXiv resolution via mcp__semantic-scholar__get_paper_details.
• Duplicate check via mcp__zotero__zotero_search_items (read-only MCP).
• Zotero write via Web API — the MCP can't write.
• Better-BibTeX export via Zotero Web API (?format=bibtex).
• Append to active project's .bib.
• File in the matching zotero_collection_key from personal_config.projects[].

Don't re-implement that logic here — invoke /cite and surface its summary back.

Search-quality heuristics

• Methodological queries ("interpretable ML for marketing", "double-machine learning for heterogeneous treatment effects") — Semantic Scholar's citation network is strongest; use get_paper_references / get_paper_citations once you've identified a seed paper.
• Preprints / recent ML ("sparse autoencoder", "GemmaScope", "SAE steering") — arXiv first; Semantic Scholar lags by 2–6 weeks on indexing.
• Marketing / consumer behavior / JMR-JCR territory — OpenAlex is broadest; supplement with Semantic Scholar.
• Author-specific ("Athey work on conformal prediction", "Imbens recent papers") — OpenAlex author.id filter; or Semantic Scholar search_authors → get_author_top_papers.
• Seed-paper expansion (DOI seed → "find papers like this") — get_paper_recommendations first, then get_paper_citations for forward, get_paper_references for backward.

Failure modes

• One source down or rate-limited: report which one, continue with the others — don't fail the whole search.
• All three sources return nothing: try a relaxed query (drop year filter, broaden terms) once. If still empty, fall back to Crossref. If still empty, surface what was tried.
• Query too vague ("find me papers", "lit review please"): ask for at least one keyword or topic before searching. Don't guess.
• Paywalled paper, no abstract available: include it in the list with <abstract not accessible> and lower-bound the score at 3 (don't auto-promote it). Note OA status no.
• Suspected duplicate that isn't (same title, different papers — common with workshop vs journal versions): keep both; tag the older version as [v1 — superseded by entry N].
• User pastes a fully-formatted citation as the query: treat that as a seed-paper anchor mode — resolve it first via DOI/title, then return related work plus the seed itself at score 5.
• OpenAlex returns 500 candidates for a broad query: tighten the keyword set rather than truncate — truncation hides relevance.

Out of scope

• Writing prose / lit-review section: that's /draft. This skill returns the input; /draft writes the section.
• Adding one paper to Zotero: that's /cite. This skill is for batches discovered through search.
• Reading PDFs in depth / extracting figures: this skill works from abstracts + metadata. For deep reads, route specific papers to a manual read or to review-paper-code if it's a methods reference.
• Comprehensive systematic review (PRISMA-style): out of scope — this is a fast-turn discovery + ranking tool, not a 6-month SR.
• Fabricating citations: never. If a paper isn't found in any of the three MCPs + Crossref, say so — don't invent author-year-title triples that "sound right".