Select and apply shortest-path algorithms for weighted graphs with negative edges.
npx skills add https://github.com/Arcadi4/nerdy --skill shortest-paths
Please help me install this Agent Skill. Skill: shortest-paths Source: https://github.com/Arcadi4/nerdy/tree/main/clrs/shortest-paths Command: npx skills add https://github.com/Arcadi4/nerdy --skill shortest-paths
Identify and select the correct shortest-path algorithm for a given graph (directed/undirected, weighted/unweighted, with possible negative edges or cycles), and produce the required outputs (distances, predecessor trees, actual paths, or cycle witnesses) under a clear production contract.
Define the problem graph and constraints, select the matching shortest-path algorithm based on preconditions, and produce the distances, paths, and reconstruction metadata with clear safety contracts.
High-intent search queries and answers about installing and using this skill.
To detect negative cycles in shortest-path problems, apply the Bellman-Ford algorithm and check for continued distance relaxation after V-1 iterations. This Skill provides cycle witnesses and explicit handling for unreachable nodes.
Dijkstra cannot handle negative edge weights. For graphs with negative edges, use Bellman-Ford or Johnson's algorithm, which supports reweighting to apply Dijkstra safely while maintaining correct shortest-path distances.
To reconstruct the actual shortest path, the algorithm maintains a predecessor tree during relaxation. This Skill outputs predecessor structures and path reconstruction metadata alongside distance arrays under a clear production contract.
Use Floyd-Warshall when you need all-pairs shortest paths or transitive closure across directed graphs. Single-source algorithms like Dijkstra or Bellman-Ford are more efficient when you only need distances from one starting node.
Yes, this approach works for directed and undirected graphs. It applies the CLRS-inspired relaxation framework to solve single-source, all-pairs, or reachability questions while accounting for graph representation and production constraints.
FrancisVargaExecute PageRank, Louvain, BFS, DFS, and shortest path algorithms on in-memory property graphs.
SPIRAL-EDWINCompute shortest paths in weighted graphs using Dijkstra, Bellman-Ford, Floyd-Warshall, and A*.
parcadeiProvides graph algorithm strategies and tool commands using Sympy and Z3.
kishorkukrejaSolves large-scale optimization problems using metaheuristic algorithms in Python.
