Literature Review

Community

lingzhi227

literature-review

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.

Overview

Publisher	lingzhi227
Repository	`agent-research-skills`
Skill name	`literature-review`
Stars	426
Forks	46
Bundled files	2
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by lingzhi227 on GitHub. Read the source before you install it.

Installation

Install the Literature Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/lingzhi227/agent-research-skills/tree/main/skills/literature-review
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/lingzhi227/agent-research-skills.git /tmp/agent-research-skills
mkdir -p .claude/skills
cp -r /tmp/agent-research-skills/skills/literature-review .claude/skills/literature-review

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Literature Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Literature Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

bash
# 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:

Persona asks a question from their unique angle
Generate search queries from the question
Search literature using the search scripts
Synthesize an answer grounded in retrieved papers with inline citations
Record the dialogue turn with search results
Repeat for 3-5 turns per persona
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:

Outline — Hierarchical topic structure
Per-section summaries — Each grounded in retrieved papers
Paper database — Structured entries for all reviewed papers
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

Upstream: literature-search, deep-research
Downstream: related-work-writing, research-planning
See also: survey-generation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Literature Review AI skill do?

Why use Literature Review on TypingMind?

Because you install it once and use it with any model. Literature Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Literature Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lingzhi227/agent-research-skills/tree/main/skills/literature-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Literature Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Literature Review?

As many as you like. As long as a model supports skills, you can use Literature Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Literature Review AI skill free?

It is published on GitHub by lingzhi227. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Literature Review

Input

References

Scripts (from literature-search skill)

Workflow

Step 1: Generate Expert Personas (from STORM)

Step 2: Multi-Perspective Dialogue

Step 3: Synthesize Knowledge

Step 4: Generate Literature Review

Output

Rules

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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