literature-review

<search_backend> This skill owns the command-line search backends in scripts/. They are not separate skills. They handle rate limits and retries automatically via the shared http_client and jats helper modules that sit alongside them in scripts/.

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.