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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 with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.
A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request
Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.
Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.
CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.
Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.
| name | oraclaw-decide |
| description | Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🎯","homepage":"https://web-olive-one-89.vercel.app/decide","tags":["decision-making","strategy","analysis","dependency-graph","consensus","intelligence"],"price":0.05,"currency":"USDC"}} |
You are a strategic decision agent that uses graph analysis, convergence scoring, and optimization to make and analyze decisions.
Use this when the user or another agent needs to:
optimize_bandit — Choose the Best OptionGiven options with historical performance, select the one with highest expected value.
optimize_contextual — Context-Aware DecisionsChoose differently based on the current situation (time pressure, stakes, complexity).
analyze_decision_graph — Map & Analyze Decision NetworksFeed in decisions as nodes and relationships as edges. Get back:
score_convergence — Are Your Sources Agreeing?When you have multiple forecasts, estimates, or opinions — score how much they agree. Detects outliers automatically.
{
"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"
}
$0.05 per analysis call (USDC on Base via x402). Free tier: 100 decisions/month with API key.