graph-algorithm-selector
Select optimal graph algorithm based on problem constraints
Best use case
graph-algorithm-selector is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Select optimal graph algorithm based on problem constraints
Teams using graph-algorithm-selector 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-selector/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How graph-algorithm-selector Compares
| Feature / Agent | graph-algorithm-selector | 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?
Select optimal graph algorithm based on problem constraints
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 Selector Skill
## Purpose
Select the optimal graph algorithm based on problem constraints, graph properties, and performance requirements.
## Capabilities
- Constraint analysis for algorithm selection
- Trade-off analysis (Dijkstra vs Bellman-Ford vs Floyd-Warshall)
- Special case detection (sparse vs dense, negative edges)
- Algorithm complexity mapping to constraints
- Suggest algorithm variants and optimizations
## Target Processes
- shortest-path-algorithms
- advanced-graph-algorithms
- graph-traversal
- graph-modeling
## Algorithm Selection Matrix
### Shortest Path
| Scenario | Algorithm | Complexity |
|----------|-----------|------------|
| Unweighted | BFS | O(V+E) |
| Non-negative weights | Dijkstra | O((V+E)log V) |
| Negative weights | Bellman-Ford | O(VE) |
| All pairs | Floyd-Warshall | O(V^3) |
| DAG | Topological + DP | O(V+E) |
### MST
| Scenario | Algorithm | Complexity |
|----------|-----------|------------|
| Sparse graph | Kruskal | O(E log E) |
| Dense graph | Prim | O(V^2) or O(E log V) |
## Input Schema
```json
{
"type": "object",
"properties": {
"problemType": {
"type": "string",
"enum": ["shortestPath", "mst", "connectivity", "flow", "matching", "traversal"]
},
"graphProperties": { "type": "object" },
"constraints": {
"type": "object",
"properties": {
"V": { "type": "integer" },
"E": { "type": "integer" },
"negativeWeights": { "type": "boolean" },
"negativeCycles": { "type": "boolean" }
}
}
},
"required": ["problemType", "constraints"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"recommendedAlgorithm": { "type": "string" },
"complexity": { "type": "string" },
"alternatives": { "type": "array" },
"reasoning": { "type": "string" }
},
"required": ["success", "recommendedAlgorithm"]
}
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