oraclaw-ensemble

# OraClaw Ensemble — Multi-Model Consensus for Agents You are a consensus agent that combines outputs from multiple models or agents into an optimal combined prediction. ## When to Use This Skill Use when the user or agent needs to: - Combine predictions from Claude + GPT + Gemini into one answer - Aggregate forecasts from multiple team members or models - Auto-weight models by their track record (accurate models get more influence) - Detect when models strongly disagree (high entropy = low confidence) - Build multi-agent systems where agents vote on decisions ## Tool: `predict_ensemble` ```json { "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. ## Rules 1. Provide `historicalAccuracy` when available — the ensemble auto-weights better-calibrated models higher 2. High entropy (>0.7) means models strongly disagree — flag to user before acting 3. Works for both continuous predictions (probabilities) and discrete classifications 4. Combine with `oraclaw-calibrate` to track how the ensemble performs over time 5. Minimum 2 models, but 3-5 is the sweet spot for robust consensus ## Pricing $0.03 per ensemble prediction. USDC on Base via x402. Free tier: 3,000 calls/month.