Literature Review

Invocation & workspace (read first): Invoke each script by its absolute path under this skill's base directory (shown to you above as "Base directory for this skill") — e.g. uv run <skill-dir>/scripts/openalex_cli.py …. Never cd into the skill directory. Stay in the directory where the user invoked the skill and anchor the review workspace there with an absolute path: compute WS="$(pwd)/review/{slug}" once at step 1 and pass $WS as --workspace everywhere. Relative review/{slug} resolves against the wrong directory and pollutes the installed plugin. In the command examples below, scripts/… is shorthand for <skill-dir>/scripts/….

Prerequisites:

• uv must be installed. Verify with uv --version. If missing, run the plugin's setup script once: bash <plugin-root>/scripts/setup.sh. The setup script installs uv, prompts (optionally) for an OpenAlex API key, and warms the dependency cache. Fallback if setup is unreachable: curl -LsSf https://astral.sh/uv/install.sh | sh && export PATH="$HOME/.local/bin:$PATH".
• OPENALEX_API_KEY (optional, recommended). Without it, OpenAlex runs in the unauthenticated polite pool — $0.01/day budget, 10 filter queries or 1 --search per day before throttling. With a free key, $1/day budget at ~10 req/s. The key lives in `/.env. **Never** read, print, cat, echo, or otherwise inspect ~/.env— credentials must stay out of the agent's context. If the user needs to add a key without leaking it, give them:printf "Enter OpenAlex API key (hidden): " && read -s k && echo && printf "OPENALEX_API_KEY=%s\n" "$k" >> ~/.env && unset k && echo "Saved."`.

1. OpenAlex — scripts/openalex_cli.py (cross-disciplinary, ~250M works) Use as the default broad search. Full reference: references/openalex/works.md, authors.md, topics.md, etc.

uv run scripts/openalex_cli.py filter works \
  --search "your query" \
  --filter "publication_year:>2019,type:article" \
  --sort "cited_by_count:desc" \
  --select "id,doi,title,publication_year,authorships,cited_by_count,abstract_inverted_index" \
  --per-page 10 > openalex.json

Raw --search alone pulls topical noise (off-topic gen-AI papers ranking high). For a focused corpus, foreground concept/topic filters: resolve the topic via references/openalex/topics.md, then narrow with --filter "topics.id:T<id>" (or concepts.id:C<id>) and use --search only to rank within that slice. Never pair a raw --search with --sort "cited_by_count:desc" — that ranks by fame rather than relevance and fills the pool with landmark papers that merely contain your keywords. Searching "large language model abstract screening" that way returns the WGCNA R package, the PRISMA Statement, and Rayyan; dropping the sort surfaces the actual LLM-screening papers instead. Sort by citations only inside an already-narrow topic filter. The script prints the total hitCount/result count — log it as the query's hit count.

2. arXiv — scripts/search_arxiv.py (preprints: CS, physics, math, quant-bio, stat) Use for very recent work, ML/CS topics, and physics. Query syntax: references/arxiv/query_syntax.md.

uv run scripts/search_arxiv.py \
  --query "ti:\"your phrase\" AND cat:cs.LG" \
  --sort_by relevance \
  --max_results 10 > arxiv.json

Default to relevance for a review — it surfaces foundational work. Use --sort_by submittedDate --sort_order descending only when you specifically want the newest preprints; date-sort biases the pool to the most recent month and misses seminal papers. Emits one JSON object with a results_count field — log that as the hit count.

3. Europe PMC — scripts/europepmc_api.py (life-science open-access full text + citation graph) Use for biomedical topics, full-text retrieval, and forward/backward citation chaining.

uv run scripts/europepmc_api.py search "your query AND HAS_FT:y" \
  --sort "CITED desc" --max_results 10 --output europmc.json
uv run scripts/europepmc_api.py get_citations MED <PMID> --output citing.json
uv run scripts/europepmc_api.py get_references MED <PMID> --output refs.json
uv run scripts/europepmc_api.py get_fulltext <PMCID> --output fulltext.txt

4. Full-text acquisition — scripts/read_paper.py (any identifier → markdown) One call per paper; resolves the best legal open-access route automatically.

uv run scripts/read_paper.py --doi 10.1038/s41586-021-03819-2 --workspace "$WS"
uv run scripts/read_paper.py --arxiv 1706.03762 --workspace "$WS"
uv run scripts/read_paper.py --pmcid PMC8371605 --workspace "$WS"

($WS is the absolute workspace from step 1 — $(pwd)/review/{slug}; never pass a relative path.) Prints one JSON line: {"status": "fulltext|abstract-only", "path": ..., "source": ..., "id": ...}. Files land in $WS/papers/{id}/ (paper.pdf, fulltext.md or abstract.md). A PDF the user drops at $WS/papers/{id}/paper.pdf is picked up before any network call. Paywalled papers return abstract-only — never scrape for them.

5. Corpus assembly — scripts/build_corpus.py (raw backend JSON → normalized, deduplicated corpus.json)

uv run scripts/build_corpus.py --openalex "$WS/openalex.json" \
  --arxiv "$WS/arxiv.json" --epmc "$WS/epmc.json" --output "$WS/corpus.json"

Each flag is repeatable. Papers found by several backends are merged into one record with a joined found_via (openalex+epmc). Running it again against an existing corpus.json adds only new papers — screening decisions, fulltext, and role on records already there are left untouched, so later search rounds and snowballing never discard prior work.

6. Citation verification — scripts/verify_citations.py (bibliography → verified/mismatched/not_found/retracted)

uv run scripts/verify_citations.py --input "$WS/refs.json"

Input: JSON array ([{"doi", "title"}] or bare strings), BibTeX (.bib), or a text/markdown file with one citation per line (DOIs extracted automatically). Resolves each through OpenAlex, then Europe PMC for DOIs, then cross-checks every clean DOI against Crossref's Retraction Watch data (OpenAlex's own retraction flag misses some withdrawn papers). Set CO_RESEARCHER_USER_AGENT="your-tool (mailto:you@example.edu)" to use Crossref's faster polite pool. Retraction is checked down a ladder — OpenAlex, then Crossref for DOIs, then the paper's PubMed record when there is no DOI (or when Crossref cannot answer). Every result carries retraction_checked and retraction_source; a check that could not run is reported as unchecked, never as clean. Prints a JSON report; exit 0 only when every citation verifies. Run it on any bibliography before presenting it — a mismatched result means the DOI exists but the claimed title doesn't match it (the classic fabrication pattern), and retracted means the paper exists but has been withdrawn; fix or drop either before output.

7. PRISMA counts — scripts/prisma_counts.py (corpus.json → PRISMA 2020 flow numbers)

uv run scripts/prisma_counts.py --corpus "$WS/corpus.json"

Reports records by source, after-dedup, screened, excluded-by-reason, included, not-retrieved, and in-synthesis. Exits 1 if any excluded record lacks a reason. Used by systematic-review; useful in any review to sanity-check that the corpus bookkeeping matches reality.

Picking a backend:

• Cross-discipline overview, citation counts, author/institution metadata → OpenAlex
• Bleeding-edge preprints in CS/ML/physics → arXiv
• Life sciences, medicine, full text, citation chaining → Europe PMC

For broad reviews, run all three in parallel and dedupe by DOI in step 3.

1. Scope — Write the research question and strict inclusion/exclusion criteria to protocol.md. Pause for user approval of the criteria. Scoping searches may revise the question; append revisions, never overwrite.
2. Search — Execute the backends. Log every query verbatim in protocol.md with date and hit count. Save raw JSON in the workspace; do not load it into context wholesale.
3. Dedupe & pool — Run build_corpus.py on the raw backend files; never hand-merge them. It emits one record per paper — key (normalized DOI, else normalized title, else a source identifier), ids, title, year, cited_by (highest any backend reported), found_via, screening: {status, stage, reason}, fulltext, role — deduplicated across backends, and re-running it after a later search round preserves every screening decision already made.

uv run scripts/build_corpus.py --openalex "$WS/openalex.json" \
  --arxiv "$WS/arxiv.json" --epmc "$WS/epmc.json" --output "$WS/corpus.json"

4. Title/abstract screening — Set screening.status (included/excluded) and reason per record. Exclusion reasons are mandatory. If the pool exceeds ~50, pilot-screen a random ~20 first and surface borderline calls to the user before bulk screening.

5. Acquire & read — For each included paper, run read_paper.py. Write the script's status value ("fulltext" or "abstract-only") into that paper's fulltext field in corpus.json immediately — prisma_counts.py reads this field to compute not_retrieved and in_synthesis, and a record left at null is counted as not retrieved. Classify each as evidence (bears directly on the question) or background. Evidence papers require notes.md written from the full text — methods and results actually read via Read/Grep on fulltext.md, one paper at a time, never multiple full texts in context. Background papers may be cited at abstract level. notes.md format: citation, read depth, design, N, key effects, claims relevant to the question (with section anchors), limitations, theme tags. Do not trust extracted tables — multi-column tables come through as scrambled line fragments; re-read the source PDF for any tabular data.

   Navigating fulltext.md depends on the route, given by the source field of the script's JSON line. source: "epmc" (JATS) yields real markdown headings — find sections with grep -n "^#". Every PDF route (arxiv_pdf, oa_pdf, user_pdf, cached) yields no # headings at all; section titles appear as bold lines, so use grep -n "^\*\*" instead. A grep "^#" returning nothing on a PDF-route paper means you used the wrong pattern, not that the document is unstructured. If neither pattern finds a section you need, cite by content rather than a section anchor.

6. Snowball — For core evidence papers, run get_references/get_citations (Europe PMC) or follow OpenAlex referenced_works. Fold the new candidates into the same corpus.json with build_corpus.py --epmc citing.json --found-via snowball:citations --output "$WS/corpus.json" (it adds only what's new, tags their provenance, and preserves decisions already made), then screen them at step 4. One round by default; stop when a round adds nothing. If included papers reveal vocabulary the original queries missed, run one adapted search round and log it.

7. Synthesize — Write synthesis.md from notes.md files only. Tag any citation whose fulltext is abstract-only with [abstract-only] inline. End with a retrieval summary listing papers not retrieved and the papers/{id}/paper.pdf path where the user can drop a legally obtained PDF for a re-run.

8. Verify bibliography — Write the final citation list to $WS/refs.json and run verify_citations.py --input "$WS/refs.json". Exit 0 is required before presenting; correct or remove any mismatched/not_found entry. (Papers screened through corpus.json will pass — this gate catches citations that entered the synthesis from memory rather than from the corpus.)

Research question: [Stated] Inclusion criteria: [Population / methods / date / language] Searches executed:

• OpenAlex: <exact query> → N hits
• arXiv: <exact query> → N hits
• Europe PMC: <exact query> → N hits

Thematic synthesis:

• [Theme 1]: [Summary with verified citations]
• [Theme 2]: [Summary with verified citations]

Research gaps:

1. [Gap with evidence of absence]
2. [Gap with evidence of absence]

Annotated bibliography:

• [Full citation with DOI/arXiv ID/PMID] — [Key contribution + quality note]

Retrieval summary: [N full-text / N abstract-only; drop-in paths for missing PDFs]