literature-review

name	literature-review
description	This skill should be used when the user asks to "do a literature review", "survey papers on a topic", "search and summarize research on X", "find papers about attention mechanisms", "systematic review of the literature", "what papers exist on Y", or wants a multi-step workflow to search, filter by relevance, score quality, and summarize academic papers using PaperBot MCP tools.
tools	["paper_search","relevance_assess","paper_judge","paper_summarize","export_to_obsidian","save_to_memory"]

Literature Review Workflow

Conduct a systematic literature review: search, filter by relevance, judge quality, summarize top papers, and save findings to memory.

Workflow

Step 1: Search for papers

Call paper_search with the research question or topic.

• Parameters: query (required), max_results (default 10; use 20–50 for broad surveys), sources (optional; omit for all sources, or specify ["arxiv", "semantic_scholar"])
• Returns: list of paper dicts with title, abstract, authors, year, venue, arxiv_id, doi

Step 2: Filter by relevance

For each paper, call relevance_assess with title, abstract, and the same query.

• Parameters: title, abstract, query, keywords (optional comma-separated terms)
• Returns: dict with score (0–100) and reason
• Suggested threshold: discard papers with score below 40
• If degraded=True, token-overlap scoring is used (less accurate but functional)

Step 3: Judge quality of relevant papers

For papers above the relevance threshold, call paper_judge.

• Parameters: title, abstract, full_text (optional), rubric (default "default"; pass the research question for context-aware judging)
• Returns: dimension scores (1–5), overall_score, recommendation (must_read / worth_reading / skim / skip)
• Prioritize papers with must_read and worth_reading recommendations

Step 4: Summarize top papers

Call paper_summarize for papers recommended as must_read or worth_reading.

• Parameters: title, abstract
• Returns: dict with summary key (concise string)
• If degraded=True, generate a manual summary from the abstract text

Step 5: Export to Obsidian (optional)

Call export_to_obsidian for papers to save as permanent Obsidian notes.

• Parameters: title, abstract, authors (list), year, venue, arxiv_id, doi (provide whichever identifiers are available)
• Returns: dict with markdown key — YAML-frontmattered note ready to write to vault

Step 6: Save synthesis to memory

Call save_to_memory with a synthesis of findings across all reviewed papers.

• Parameters: content (synthesis text), kind ("note" for general observations, "hypothesis" for research directions), user_id (default "default"), scope_type ("global" unless scoping to a specific research track), scope_id (required if scope_type="track"), confidence (0.0–1.0)
• Returns: dict with created or skipped status

Degraded Mode

paper_judge, paper_summarize, and relevance_assess require a configured LLM API key. paper_search works without LLM and returns raw search results in all cases.

When any LLM-backed tool returns degraded=True:

• The response also contains an error key describing the issue
• Set OPENAI_API_KEY or ANTHROPIC_API_KEY and restart the MCP server
• In degraded mode, proceed with paper_search results only; skip Steps 2–4

Notes

• For broad surveys (>30 papers), consider running relevance_assess in bulk before paper_judge to reduce LLM calls
• Use rubric="reproducibility" in paper_judge if the review goal is identifying reproducible papers for implementation
• The export_to_obsidian step is optional — skip it if the user has not set up an Obsidian vault or does not need persistent notes