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.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill calibrate-probability-forecast -a windsurf -y
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.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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