agentsclimarketplace

Literature review

Skill dongzhigang13305312738-art/paper-skills/literature-review

📝 124 个 SCI/SSCI 论文写作 AI 技能合集：选题→文献综述→实验→图表→写作→润色→去AI痕迹→投稿，一个 paper-studio 总入口全流程调度。支持 Claude Code / WorkBuddy。

Install
npx -y skills add dongzhigang13305312738-art/paper-skills --skill literature-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

3 things to look at

  • 16 days oldThe repository was created 16 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.

SKILL.md

3.3 KB, as published. Nobody here has run it

Literature Review

Conduct deep literature reviews through multi-perspective dialogue and systematic search.

Input

  • $0 — Research topic or question
  • $1 — Optional: specific focus or angle

References

  • Multi-perspective dialogue prompts (STORM): ~/.claude/skills/literature-review/references/dialogue-prompts.md
  • Literature review workflow (AgentLaboratory): ~/.claude/skills/literature-review/references/review-workflow.md

Scripts (from literature-search skill)

# Search Semantic Scholar
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py --query "topic" --max-results 20

# Search OpenAlex
python ~/.claude/skills/literature-search/scripts/search_openalex.py --query "topic" --max-results 20

# Search arXiv
python ~/.claude/skills/deep-research/scripts/search_arxiv.py --query "topic" --max-results 10

Workflow

Step 1: Generate Expert Personas (from STORM)

Given the topic, create 3-5 diverse expert personas:

  • Each represents a different perspective, role, or research angle
  • Example: "ML systems researcher focused on efficiency", "Theoretical statistician concerned with guarantees"
  • Use the persona generation prompts from references

Step 2: Multi-Perspective Dialogue

For each persona, simulate a multi-turn Q&A conversation:

  1. Persona asks a question from their unique angle
  2. Generate search queries from the question
  3. Search literature using the search scripts
  4. Synthesize an answer grounded in retrieved papers with inline citations
  5. Record the dialogue turn with search results
  6. Repeat for 3-5 turns per persona
  7. End when persona says "Thank you so much for your help!"

Step 3: Synthesize Knowledge

  • Combine all persona conversations into a unified knowledge base
  • Remove redundancy across personas
  • Organize by theme/subtopic
  • Generate an outline based on the collected information

Step 4: Generate Literature Review

  • Write a structured review organized by the generated outline
  • Every claim must be supported by a citation
  • Include a summary table of key papers (method, contribution, limitations)

Output

A structured literature review with:

  1. Outline — Hierarchical topic structure
  2. Per-section summaries — Each grounded in retrieved papers
  3. Paper database — Structured entries for all reviewed papers
  4. Knowledge gaps — Identified areas needing further investigation

Rules

  • Every sentence in the review must be supported by gathered information
  • If information is not found, explicitly state the gap
  • Cite broadly — cover diverse approaches, not just the most popular
  • Include recent papers (last 2-3 years) alongside foundational work
  • Use inline citations: "Smith et al. [1] propose..."

Related Skills

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.