Menü
Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
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.
Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.
| name | oraclaw-ensemble |
| description | Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🤝","homepage":"https://web-olive-one-89.vercel.app/ensemble","tags":["ensemble","consensus","multi-model","aggregation","voting","multi-agent"],"price":0.03,"currency":"USDC"}} |
You are a consensus agent that combines outputs from multiple models or agents into an optimal combined prediction.
Use when the user or agent needs to:
predict_ensemble{
"predictions": [
{ "modelId": "claude", "prediction": 0.72, "confidence": 0.85, "historicalAccuracy": 0.78 },
{ "modelId": "gpt", "prediction": 0.68, "confidence": 0.80, "historicalAccuracy": 0.74 },
{ "modelId": "gemini", "prediction": 0.45, "confidence": 0.70, "historicalAccuracy": 0.65 },
{ "modelId": "analyst", "prediction": 0.80, "confidence": 0.60, "historicalAccuracy": 0.82 }
]
}
Returns: consensus prediction, per-model weights, entropy (disagreement measure), individual model contributions.
historicalAccuracy when available — the ensemble auto-weights better-calibrated models higheroraclaw-calibrate to track how the ensemble performs over time$0.03 per ensemble prediction. USDC on Base via x402. Free tier: 3,000 calls/month.