genpark-page-rank-power-iteration-centrality-skill

genpark-page-rank-power-iteration-centrality-skill

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⚡ Overview & Architectural Significance

genpark-page-rank-power-iteration-centrality-skill delivers zero-dependency graph pathfinding, topological dependency resolution, network maximum flow, and centrality ranking engineered strictly using Python 3.9+ standard library.

🌟 Key Architectural Capabilities

• Zero External Dependencies: Operates exclusively via pure Python (heapq, collections, math, json). Zero NetworkX or SciPy build overhead.
• Enterprise Graph Invariants: Implements formal Dijkstra/A* priority queue path traversal, Kahn's DAG topological sorting, Edmonds-Karp BFS residual flow augmentation, Kruskal's disjoint-set minimum spanning tree, and PageRank random surfer power iteration.
• Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.

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🏗️ Architectural Topology & State Machine

flowchart TD
    GraphInput["Graph Topology: Nodes & Weighted Edges"] --> AlgorithmRouter["Graph & Network Routing Kernel"]
    AlgorithmRouter --> Pathfinder["Dijkstra & A* Shortest Pathfinder"]
    AlgorithmRouter --> DAGAnalyzer["Topological Sorter & Dependency Resolver"]
    AlgorithmRouter --> FlowSolver["Edmonds-Karp Maximum Flow Solver"]
    AlgorithmRouter --> MSTBuilder["Kruskal's Minimum Spanning Tree"]
    AlgorithmRouter --> CentralityEngine["PageRank Authority & Centrality"]
    Pathfinder --> ExecutionPlan["Optimal Multi-Agent Execution Plan"]
    DAGAnalyzer --> ExecutionPlan
    FlowSolver --> ExecutionPlan
    MSTBuilder --> ExecutionPlan
    CentralityEngine --> ExecutionPlan

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🚀 Quickstart & Standalone Execution

Local Python Client Usage

from client import PageRankCentrality

# Initialize engine
engine = PageRankCentrality()

# Execute self-testing benchmark suite
result = engine.benchmark_pagerank()
print("Execution Result:", result)

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🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claude_desktop_config.json or cursor.json:

{
  "mcpServers": {
    "genpark-page-rank-power-iteration-centrality-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-page-rank-power-iteration-centrality-skill/mcp_server.py"]
    }
  }
}

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📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

pip install git+https://github.com/alphaparkinc/genpark-page-rank-power-iteration-centrality-skill.git

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