Agent Skills

literature-review

researchlingzhi2273.8K installs

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.

Install

npx skills add https://github.com/lingzhi227/agent-research-skills --skill literature-review
SKILL.md

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

Related skills

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers