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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.
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Basé sur la classification professionnelle SOC
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.