Agent skill · AI & Agents

network-optimizer

Network optimization skill for transportation, assignment, and flow problems on graph structures.

a5c-ai1,642★ · 1 repos on radarProfile →
claude-codecodexcan modify filesMIT
Install
npx skills add a5c-ai/babysitter --skill network-optimizer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0.0
Declared author: babysitter-sdk
Allowed tools: Bash(*)ReadWriteEditGlobGrepWebFetch
Path: library/specializations/domains/science/industrial-engineering/skills/network-optimizer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,642
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# network-optimizer You are **network-optimizer** - a specialized skill for solving network optimization problems including shortest paths, minimum spanning trees, maximum flows, and assignment problems. ## Overview This skill enables AI-powered network optimization including: - Shortest path algorithm selection (Dijkstra, Bellman-Ford, Floyd-Warshall) - Minimum spanning tree generation - Maximum flow / minimum cut analysis - Minimum cost network flow modeling - Assignment problem solving (Hungarian algorithm) - Network simplex implementation - Multi-commodity flow modeling ## Prerequisites - Python 3.8+ with NetworkX installed - Google OR-Tools for advanced problems - Understanding of graph theory ## Capabilities ### 1. Shortest Path Algorithms ```python import networkx as nx def shortest_path_analysis(G, source, target): """ Select and apply appropriate shortest path algorithm """ # Check for negative weights has_negative = any(d.get('weight', 1) < 0 for u, v, d in G.edges(data=True)) if not has_negative: # Dijkstra for non-negative weights path = nx.dijkstra_path(G, source, target) length = nx.dijkstra_path_length(G, source, target) else: # Bellman-Ford for negative weights path

What's inside
Steps it walks through
  1. Overview
  2. Prerequisites
  3. Capabilities
  4. 1. Shortest Path Algorithms
  5. 2. Minimum Spanning Tree
  6. 3. Maximum Flow / Minimum Cut
  7. 4. Minimum Cost Flow
  8. 5. Assignment Problem (Hungarian Algorithm)
  9. 6. Multi-Commodity Flow
  10. Process Integration
  11. Output Format
  12. Tools/Libraries
  13. Best Practices
  14. Constraints
More from babysitter
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About this skill
What does the network-optimizer skill do?

Network optimization skill for transportation, assignment, and flow problems on graph structures.

How do I install it?

Run `npx skills add a5c-ai/babysitter --skill network-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going