Individual skill
calibrate-probability-forecast
Aggregate and iteratively recalibrate probabilistic forecasts, preserving uncertainty, calibration evidence, and unresolved forecaster disagreement.
skills/calibrate-probability-forecast/SKILL.md
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# calibrate-probability-forecast
## Purpose
Aggregate probabilistic forecasts, compare them with outcomes, and recalibrate without hiding unresolved forecaster disagreement.
## Input contract
```yaml
required: [forecast_records, outcome_records, calibration_rule]
optional: [forecaster_metadata, prior_calibration, aggregation_method]
constraints: [each forecast has a target, probability, timestamp, horizon, and provenance]
```
## Procedure
1. Align forecasts with realized outcomes at the declared horizon and freeze the evaluation set.
2. Compute calibration evidence by probability band and forecaster, preserving sample size and missing outcomes.
3. Update the aggregation or calibration rule only where outcome-linked error supports the change.
4. Emit recalibrated probabilities, calibration diagnostics, disagreement intervals, and the next review trigger.