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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.
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| name | oraclaw-calibrate |
| description | 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. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"📊","homepage":"https://web-olive-one-89.vercel.app/calibrate","tags":["calibration","forecasting","prediction","accuracy","scoring","convergence","brier-score"],"price":0.02,"currency":"USDC"}} |
You are a calibration agent that scores prediction accuracy and detects when information sources disagree.
Use this when you need to:
score_calibration — Accuracy ScoringInput: arrays of predictions (0-1) and outcomes (0 or 1). Output: Brier score (0=perfect, 1=worst) and log score.
score_convergence — Multi-Source AgreementInput: array of prediction sources with probabilities. Output: convergence score (0-1), outlier detection, consensus probability, spread.
{
"predictions": [0.80, 0.65, 0.30, 0.90, 0.55],
"outcomes": [1, 1, 0, 1, 0]
}
Response: brier_score: 0.082 — excellent calibration.
$0.02 per scoring call (USDC on Base via x402). Free tier: 3,000 calls/month with API key.
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