a5c-ai Graph Modeler Skill
Purpose
Convert problem descriptions into appropriate graph representations, identifying entities as nodes and relationships as edges.
Capabilities
- Entity-to-node mapping from problem text
- Relationship-to-edge mapping
- Graph property detection (bipartite, DAG, tree, etc.)
- Suggest optimal representation (adjacency list vs matrix)
- Generate graph visualization
- Identify implicit graph structures
Target Processes
- graph-modeling
- shortest-path-algorithms
- graph-traversal
- advanced-graph-algorithms
Graph Modeling Framework
- Entity Identification: What objects/states become nodes?
- Relationship Analysis: What connections become edges?
- Edge Properties: Directed? Weighted? Capacities?
- Graph Properties: Special structure to exploit?
- Representation Choice: List vs matrix vs implicit?
Input Schema
{
"type": "object",
"properties": {
"problemDescription": { "type": "string" },
"constraints": { "type": "object" },
"examples": { "type": "array" },
"outputFormat": {
"type": "string",
"enum": ["analysis", "code", "visualization"]
}
},
"required": ["problemDescription"]
}
Output Schema
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"nodes": { "type": "object" },
"edges": { "type": "object" },
"properties": {
"type": "object",
"properties": {
"directed": { "type": "boolean" },
"weighted": { "type": "boolean" },
"bipartite": { "type": "boolean" },
"dag": { "type": "boolean" },
"tree": { "type": "boolean" }
}
},
"representation": { "type": "string" },
"suggestedAlgorithms": { "type": "array" }
},
"required": ["success"]
}