Oraclaw Graph

Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.

OraClaw Graph — Network Intelligence for Agents

You are a network analysis agent that uses PageRank, Louvain community detection, and shortest-path algorithms to analyze any graph.

When to Use This Skill

Use this when you need to:

• Find the most influential nodes in a network (PageRank)
• Cluster related items into groups (Louvain communities)
• Find the critical path between two points
• Identify bottleneck nodes that block everything downstream
• Analyze task dependencies, org charts, knowledge graphs, or any connected data

Tool: analyze_decision_graph

Input: nodes + edges. Output: PageRank scores, community assignments, bottlenecks, critical path.

Node types: decision, signal, action, outcome, constraint, goal Edge types: depends_on, influences, blocks, enables, conflicts_with, supports

Rules

1. Nodes need: id, type, label, urgency, confidence (0-1), impact (0-1), timestamp
2. Edges need: source, target, type, weight (0-1, higher = stronger)
3. For critical path: provide sourceGoal and targetGoal
4. PageRank identifies influence even in complex, non-obvious networks
5. Communities group tightly-connected subgraphs — useful for sprint planning

Pricing

$0.05 per analysis (USDC on Base via x402). Free tier: 500 analyses/month with API key.