abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Aggregate and iteratively recalibrate probabilistic forecasts, preserving uncertainty, calibration evidence, and unresolved forecaster disagreement.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/calibrate-probability-forecastContext 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.
name: calibrate-probability-forecast description: "Aggregate and iteratively recalibrate probabilistic forecasts, preserving uncertainty, calibration evidence, and unresolved forecaster disagreement."
Aggregate probabilistic forecasts, compare them with outcomes, and recalibrate without hiding unresolved forecaster disagreement.
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]
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.
produces: [calibrated_forecasts, calibration_diagnostics, disagreement_report, review_trigger] delta_fields: [findings, evidence_updates, hypothesis_updates, uncertainties, decisions, open_questions]
Do not score forecasts whose outcomes are not yet observable. A changed probability without new outcome-linked evidence is a model revision, not calibration.
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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