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/calibrate-probability-forecast

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

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de-anthropocentric-research-engine
499200 skills
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/calibrate-probability-forecast

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

calibrate-probability-forecast.SKILL.md
name: calibrate-probability-forecast
description: "Aggregate and iteratively recalibrate probabilistic forecasts, preserving uncertainty, calibration evidence, and unresolved forecaster disagreement."

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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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine