connected-papers-mapper
Citation graph exploration for discovering related work through visual graph traversal
Best use case
connected-papers-mapper is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Citation graph exploration for discovering related work through visual graph traversal
Teams using connected-papers-mapper should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/connected-papers-mapper/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How connected-papers-mapper Compares
| Feature / Agent | connected-papers-mapper | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Citation graph exploration for discovering related work through visual graph traversal
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.
SKILL.md Source
# Connected Papers Mapper ## Purpose Provides citation graph exploration capabilities for discovering related work through visual graph traversal and temporal analysis. ## Capabilities - Citation graph generation - Seminal paper identification - Research front detection - Temporal citation analysis - Field bridge identification - Export to reference managers ## Usage Guidelines 1. **Seed Papers**: Start with key papers in the field 2. **Graph Exploration**: Navigate citation relationships 3. **Temporal Analysis**: Track field evolution over time 4. **Bridge Detection**: Find cross-disciplinary connections ## Tools/Libraries - Connected Papers API - NetworkX - pyvis
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