graph-algorithm-selector

Select optimal graph algorithm based on problem constraints

509 stars

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

$curl -o ~/.claude/skills/graph-algorithm-selector/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/algorithms-optimization/skills/graph-algorithm-selector/SKILL.md"

Manual Installation

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

How graph-algorithm-selector Compares

Feature / Agentgraph-algorithm-selectorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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