Best use case
graph-algorithm-library is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive graph algorithms implementation
Teams using graph-algorithm-library 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/graph-algorithm-library/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How graph-algorithm-library Compares
| Feature / Agent | graph-algorithm-library | 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?
Comprehensive graph algorithms implementation
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
# Graph Algorithm Library ## Purpose Provides comprehensive graph algorithms for combinatorial analysis and network computations. ## Capabilities - Shortest path algorithms (Dijkstra, Bellman-Ford, Floyd-Warshall) - Network flow algorithms - Matching algorithms - Graph coloring - Planarity testing - Graph isomorphism ## Usage Guidelines 1. **Representation**: Choose appropriate graph representation 2. **Algorithm Selection**: Match algorithm to problem structure 3. **Complexity Analysis**: Consider time/space tradeoffs 4. **Sparse Graphs**: Use specialized algorithms for sparse graphs ## Tools/Libraries - NetworkX - igraph - LEMON - Boost Graph Library
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