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yogsoth-aiyogsoth-ai/de-anthropocentric-research-engine/skills/calibrate-probability-forecast/SKILL.md

calibrate-probability-forecast

Skill505 starsChanged 49 days agoApache-2.0View on GitHub

Aggregate and iteratively recalibrate probabilistic forecasts, preserving uncertainty, calibration evidence, and unresolved forecaster disagreement.

1 other project uses a version of this.

Adopted by

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The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Update the aggregation or calibration rule only where outcome-linked error supports the change.

    line 18
  2. Distinctive:

    Never collapse unresolved disagreement into a single certainty value.

    line 27
  3. Distinctive:

    Do not score forecasts whose outcomes are not yet observable.

    line 29

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

calibrate-probability-forecast

Purpose

Aggregate probabilistic forecasts, compare them with outcomes, and recalibrate without hiding unresolved forecaster disagreement.

Input contract

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.

Output contract

produces: [calibrated_forecasts, calibration_diagnostics, disagreement_report, review_trigger]
delta_fields: [findings, evidence_updates, hypothesis_updates, uncertainties, decisions, open_questions]

Quality gates

  • A-class gate: declared universe = all closed forecasts in the evaluation horizon; numerator = forecasts with outcome linkage and valid timestamp/probability; batch increment = one closed forecast; stopping reason = calibration error stabilizes or the closed set is exhausted; source references = forecast/outcome IDs and calibration runs; direction/threshold reason = recalibration moves probabilities toward observed frequencies only when the declared scoring rule shows systematic error.
  • Never collapse unresolved disagreement into a single certainty value.

Failure and counterexamples

Do not score forecasts whose outcomes are not yet observable. A changed probability without new outcome-linked evidence is a model revision, not calibration.

Provenance map

  • resolved: futures-calibration

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