Directed acyclic graph (DAG) topological sorting and Tarjan's strongly connected components (SCC) engine for agent dependency resolution.
genpark-topological-sorter-tarjan-scc-skill
Production-Grade Graph Theory & Network Flow Agent Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
genpark-topological-sorter-tarjan-scc-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.
🏗️ Architectural Topology & State Machine
🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claudedesktopconfig.json or cursor.json:
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
Maintained with ❤️ by GenPark AI Engineering • Powering Graph Intelligence in Autonomous Agents 🌍
From the project README.
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Directed acyclic graph (DAG) topological sorting and Tarjan's strongly connected components (SCC) engine for agent dependency resolution.
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