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Algorithms

Solve graph, search, and optimization problems using standard algorithmic methods.

3|Updated Apr 18, 2026
Search Tags:#optimization#algorithms#data structures#graph analysis#pathfinding#dynamic programming
One-click install
npx skills add https://github.com/garochee33/DSH --skill algorithms-garochee33
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Algorithms
Source: https://github.com/garochee33/DSH/tree/main/kb/skills/algorithms
Command: npx skills add https://github.com/garochee33/DSH --skill algorithms-garochee33

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you choose and apply the right algorithm when a problem needs efficient search, optimization, graph reasoning, or structured computation instead of ad hoc trial and error.

Core Features & Use Cases

  • Graph analysis: Find shortest paths, spanning trees, and graph centrality for routing, networks, and dependency mapping.
  • Search and optimization: Use sorting, searching, dynamic programming, A*, and stochastic methods to solve planning and decision problems.
  • Core data structures: Work with trees, heaps, tries, and topological ordering for reliable algorithmic workflows.
  • Use Case: If you need to rank connected systems, compare paths, or optimize a solution under constraints, this Skill provides the standard methods and outputs.

Quick Start

Ask for the shortest path, MST, or another algorithmic result you need from your graph or dataset and specify the inputs and desired output format.

Frequently Asked Questions about Algorithms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find the shortest path or minimum spanning tree in a network graph?▼

You can solve graph optimization problems by providing structured network inputs and requesting specific outputs like shortest paths, minimum spanning trees, or centrality measures, which are computed using networkx and scipy.

What is the best way to optimize routing and dependency mapping across DAGs?▼

The best way to optimize routing and dependency mapping across DAGs is to apply topological orderings and graph centrality algorithms to structured data, yielding decision optimization and reliable dependency analysis.

Does this Skill support dynamic programming and A* search for planning problems?▼

Yes, dynamic programming and A* search are supported alongside stochastic methods to solve planning and decision problems, optimizing solutions under constraints using structured computation.

Can I use networkx and scipy to compute graph centrality and topological orderings here?▼

Yes, this Skill leverages networkx, scipy, and numpy to compute graph centrality, topological orderings, entropy, and heuristics across trees, arrays, and DAGs for ranking connected systems.

When do I need dynamic programming instead of ad hoc trial and error for optimization?▼

You need dynamic programming instead of ad hoc trial and error when a problem requires efficient search, graph reasoning, or structured computation to optimize solutions under strict constraints.

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