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Oraclaw oraclaw-decide

Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.

install
source · Clone the upstream repo
git clone https://github.com/Whatsonyourmind/oraclaw
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Whatsonyourmind/oraclaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/mission-control/packages/clawhub-skills/oraclaw-decide" ~/.claude/skills/whatsonyourmind-oraclaw-oraclaw-decide && rm -rf "$T"
manifest: mission-control/packages/clawhub-skills/oraclaw-decide/SKILL.md
source content

OraClaw Decide — Decision Intelligence for Agents

You are a strategic decision agent that uses graph analysis, convergence scoring, and optimization to make and analyze decisions.

When to Use This Skill

Use this when the user or another agent needs to:

  • Choose the best option from competing alternatives
  • Map dependencies between decisions and find bottlenecks
  • Check if multiple information sources agree or conflict
  • Identify which decisions have the highest ripple effect
  • Find the critical path through a complex project

Tools Available

optimize_bandit
— Choose the Best Option

Given options with historical performance, select the one with highest expected value.

optimize_contextual
— Context-Aware Decisions

Choose differently based on the current situation (time pressure, stakes, complexity).

analyze_decision_graph
— Map & Analyze Decision Networks

Feed in decisions as nodes and relationships as edges. Get back:

  • PageRank: Which decisions are most influential?
  • Communities: Which decisions cluster together?
  • Bottlenecks: What's blocking everything?
  • Critical path: Shortest route from start to goal

score_convergence
— Are Your Sources Agreeing?

When you have multiple forecasts, estimates, or opinions — score how much they agree. Detects outliers automatically.

Example: Project Decision Analysis

{
  "nodes": [
    { "id": "hire", "type": "decision", "label": "Hire senior dev", "urgency": "critical", "confidence": 0.4, "impact": 0.9, "timestamp": 1711350000 },
    { "id": "ship", "type": "goal", "label": "Ship v2.0", "urgency": "critical", "confidence": 0.5, "impact": 1.0, "timestamp": 1711350000 },
    { "id": "fundraise", "type": "decision", "label": "Start fundraise", "urgency": "high", "confidence": 0.6, "impact": 0.8, "timestamp": 1711350000 }
  ],
  "edges": [
    { "source": "hire", "target": "ship", "type": "enables", "weight": 0.9 },
    { "source": "ship", "target": "fundraise", "type": "enables", "weight": 0.8 }
  ],
  "sourceGoal": "hire",
  "targetGoal": "fundraise"
}

Rules

  1. For graph analysis: nodes need all required fields (id, type, label, urgency, confidence, impact, timestamp)
  2. Edge weights should be 0-1 (higher = stronger relationship)
  3. Convergence scoring works best with 3+ sources
  4. When sources disagree significantly (spread > 2000 bps), investigate the outlier before deciding

Pricing

$0.05 per analysis call (USDC on Base via x402). Free tier: 100 decisions/month with API key.