skills/write-review-paper/SKILL.md
# Literature Review Writing Skill Overview This Claude Code skill guides users through writing structured literature reviews and survey papers from pre-collected research materials. ## Key Purpose The skill helps organize papers, synthesize findings, and produce academic writing—but specifically **not** for finding new papers or generating novel research ideas. ## Main Workflow (4 Phases) **Phase 1: Reading Strategy** Users triage papers into priority levels (P1-P3) and create a reading plan
npx skillsauth add lamm-mit/scienceclaw skills/write-review-paperInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This Claude Code skill guides users through writing structured literature reviews and survey papers from pre-collected research materials.
The skill helps organize papers, synthesize findings, and produce academic writing—but specifically not for finding new papers or generating novel research ideas.
Phase 1: Reading Strategy Users triage papers into priority levels (P1-P3) and create a reading plan with standardized note templates for each source.
Phase 2: Synthesis & Organization This phase involves building comparison tables across papers, creating timeline analyses showing research evolution, and developing taxonomy structures to categorize approaches by multiple dimensions.
Phase 3: Writing Structure Templates guide users toward either survey paper format or thesis chapter structure, with clear section recommendations.
Phase 4: Writing Tips The skill provides citation density guidelines (introduction: 10-20 citations; main survey: 50-100+ citations), transition phrases for connecting ideas, and common pitfalls like "列举式写作" (list-style writing that lacks analytical depth).
All materials go to $W/review/ and include reading plans, annotated notes, comparison matrices, timelines, taxonomies, draft documents, and bibliography files.
tools
Onboard and manage Paperclip AI for research-paper knowledge and agent orchestration
development
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
testing
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
development
Execute arbitrary Python code and return stdout. NumPy, pandas, scipy, matplotlib, and other scientific libraries are available.