メニュー
Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph"
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
Use when "codex", "use gpt", "gpt-5", "openai codex", "let openai", "full-auto", "autonomous code generation"
Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
| name | networkx |
| description | Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph" |
| version | 1.0.0 |
Python library for creating, analyzing, and visualizing networks and graphs.
| Type | Edges | Multiple Edges |
|---|---|---|
Graph | Undirected | No |
DiGraph | Directed | No |
MultiGraph | Undirected | Yes |
MultiDiGraph | Directed | Yes |
| Measure | What It Finds | Use Case |
|---|---|---|
| Degree | Most connections | Popular nodes |
| Betweenness | Bridge nodes | Information flow |
| Closeness | Fastest reach | Efficient spreaders |
| PageRank | Importance | Web pages, citations |
| Eigenvector | Influential connections | Who knows important people |
| Algorithm | Purpose |
|---|---|
| Shortest path | Minimum hops |
| Weighted shortest | Minimum cost |
| All pairs shortest | Full distance matrix |
| Dijkstra | Efficient weighted paths |
| Method | Approach |
|---|---|
| Louvain | Modularity optimization |
| Greedy modularity | Hierarchical merging |
| Label propagation | Fast, scalable |
| Generator | Model |
|---|---|
| Erdős-Rényi | Random edges |
| Barabási-Albert | Preferential attachment (scale-free) |
| Watts-Strogatz | Small-world |
| Complete | All connected |
| Layout | Best For |
|---|---|
| Spring | General purpose |
| Circular | Regular structure |
| Kamada-Kawai | Aesthetics |
| Spectral | Clustered graphs |
| Format | Preserves Attributes | Human Readable |
|---|---|---|
| GraphML | Yes | Yes (XML) |
| Edge list | No | Yes |
| JSON | Yes | Yes |
| Pandas | Yes | Via DataFrame |
| Scale | Approach |
|---|---|
| < 10K nodes | Any algorithm |
| 10K - 100K | Use approximate algorithms |
| > 100K | Consider graph-tool or igraph |
Key concept: NetworkX is pure Python - great for prototyping, may need alternatives for production scale.
