Living Review

Maintains a continuously updated, structured literature review for a research team, synthesizing findings from multiple sources and generating living documents as new work is published.

150 stars
Complexity: medium

About this skill

The Living Review AI Agent Skill is designed to automate and streamline the process of maintaining a dynamic, continuously updated literature review for research teams. It acts as a central hub for all research papers, ingesting new work from various sources like PDFs, DOIs, arXiv, and PubMed IDs into a shared knowledge base. This skill synthesizes findings across a team's collective reading, extracts critical information such as claims, methods, datasets, and limitations, and intelligently groups papers by themes or recency. Researchers would use this skill to efficiently manage their ongoing literature review, ensuring it evolves with the latest publications. It's invaluable for tasks like rapidly updating existing reviews, adding new papers with contextual tags, getting quick summaries of team-read literature on specific topics, or providing new team members with a comprehensive overview of prior work. Furthermore, it significantly aids in preparing manuscript introductions or related work sections by generating structured Markdown or LaTeX drafts. Its primary benefit lies in fostering collaborative research and maintaining an up-to-date, traceable knowledge base. By tracking contributors, flagging conflicting or extending papers, and allowing diffs against previous versions, the Living Review skill transforms a typically static and laborious process into a dynamic, intelligent, and collaborative endeavor, ensuring research teams always have access to the most current and relevant scientific landscape.

Best use case

Living Review is best used when you need a repeatable data & research workflow instead of a one-off prompt. It is especially useful for teams working in multi. Maintains a continuously updated, structured literature review for a research team, synthesizing findings from multiple sources and generating living documents as new work is published.

Maintains a continuously updated, structured literature review for a research team, synthesizing findings from multiple sources and generating living documents as new work is published.

Users should expect a more consistent data & research output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "Living Review" skill to help with this data & research task. Context: Maintains a continuously updated, structured literature review for a research team, synthesizing findings from multiple sources and generating living documents as new work is published.

Example output

A structured data & research result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
  • Use it when you are solving a data & research task and want a more structured operating flow.
  • Use it when you can invest a small amount of setup effort for a more repeatable workflow.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/living-review/SKILL.md --create-dirs "https://raw.githubusercontent.com/meowscles69/PaperClaw/main/skills/literature/living-review/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/living-review/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Living Review Compares

Feature / AgentLiving ReviewStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexitymediumN/A

Frequently Asked Questions

What does this skill do?

Maintains a continuously updated, structured literature review for a research team, synthesizing findings from multiple sources and generating living documents as new work is published.

How difficult is it to install?

The installation complexity is rated as medium. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Living Review

## Overview
Maintains a continuously updated, structured literature review for a research team. Ingests papers from multiple sources, synthesizes findings across the team's collective reading, and produces a living document that evolves as new work is published.

## When to Use
- User asks to "update our literature review" or "add this paper to the review"
- User wants a summary of what their team has read on a topic
- User asks "what do we know about X based on our papers?"
- Onboarding a new team member who needs a fast overview of prior work
- Preparing a manuscript introduction or related work section

## Key Capabilities
- Ingest PDFs, DOIs, arXiv IDs, or PubMed IDs into a shared knowledge base
- Extract key claims, methods, datasets, and limitations per paper
- Auto-group papers by theme, methodology, or recency
- Generate a structured Markdown or LaTeX review draft
- Track which team member added which paper and when
- Flag papers that conflict with or extend each other
- Diff the review against previous versions to show what changed

## Usage Examples

### Add a paper to the living review
```python
review.add_paper(
    doi="10.1038/s41586-024-00001-0",
    added_by="alice",
    tags=["transformer", "protein-folding", "benchmark"]
)
```

### Generate a living review draft on a topic
```python
review.generate_draft(
    topic="attention mechanisms in protein language models",
    format="latex",
    max_papers=40,
    include_team_notes=True
)
```

### Show what changed since last week
```python
review.diff(since="2024-01-01", show_new_papers=True, show_updated_claims=True)
```

## Output Format
Produces structured Markdown with sections: Background, Key Methods, Datasets Used, Open Questions, Recent Additions. Each claim is traceable to a source paper and team contributor.

## Notes
- Works best when combined with `arxiv-monitor` and `semantic-scholar` skills for automatic ingestion
- Team notes and annotations are preserved across updates — never overwritten
- Supports BibTeX export for manuscript preparation

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