- 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
- primaryEnv
- ORACLAW_API_KEY
- emoji
- 🤝
- homepage
- https://oraclaw.dev/ensemble
- tags
- price
- 0.03
- currency
- USDC
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
{
"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
- Provide
historicalAccuracywhen available — the ensemble auto-weights better-calibrated models higher - High entropy (>0.7) means models strongly disagree — flag to user before acting
- Works for both continuous predictions (probabilities) and discrete classifications
- Combine with
oraclaw-calibrateto track how the ensemble performs over time - 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.