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SkillsMP has collected 9 skills from kennethkhoocy/lit-review-orchestrator. Open a skill to review its source and details.
Showing 9 of 9 collected skills.
Master controller for the lit-review pipeline, driven by a document. Give it a .tex or .docx file describing an article — a full manuscript, an abstract, or a proposal — and it extracts a search plan, runs an Undermind deep search (through the Undermind MCP…
Run the lit-review orchestrator free index search channel against keyless OpenAlex, Crossref, and Semantic Scholar keyword-search endpoints. Use when the user invokes the free search channel, asks for keyless scholarly index search, or needs Stage 4e fallback…
Stage 5 of the lit review pipeline: merge and deduplicate papers from all prior stages (Undermind, Scholar Labs, supplementary search) into a single master list with provenance tracking. Two-pass dedup: exact DOI match then LLM fuzzy match via…
Stage 6 of the lit review pipeline: screen paper abstracts against the research prompt. The orchestrator's agent-driven flow runs this re-ranker on Opus subagents; a standalone run uses the in-script Claude Sonnet API fallback. Rates relevance 1-10, tags each…
Stage 2 of the lit review pipeline: run a Google Scholar Labs deep search from the detailed research question produced by Stage 0, scrape each result's citation (Cite to BibTeX), then parse + enrich into the pipeline schema. The driver signs in to Google with…
Stage 4 of the lit review pipeline: search academic sources — general Google Scholar (--scholar), SSRN, NBER, HeinOnline, citation chaining via Semantic Scholar, and forthcoming paper lists from top finance journals. Long research prompts are automatically…
Stage 1 of the lit review pipeline: run an Undermind deep search from the natural-language brief produced by Stage 0 through the Undermind MCP server (agent-driven; no browser, no stored credentials), then parse + enrich the returned papers into the pipeline…
Run the lit-review orchestrator keyless agent-driven web search channel that uses WebSearch and WebFetch outputs normalized through websearch_ingest.py. Use when the user invokes the web search channel, asks for Stage 4d open-web literature discovery, or…
Stage 2b of the lit review pipeline: run a Google Gemini Deep Research deep search (Interactions API) from the brief produced by Stage 0, then parse the cited report into the pipeline schema. API-driven (GEMINI_API_KEY), no browser. An alternative deep-search…