Tracks and audits AI research predictions over time to evaluate accuracy and predictor reliability.
The Prediction Tracking skill provides a rigorous framework for Claude to document, monitor, and evaluate forecasts made by AI researchers and critics. It enables users to record specific claims with mandatory metadata like timeframes and confidence levels, then later audit those claims against emerging evidence. By generating accuracy scores and assessing 'calibration'—the relationship between a predictor's confidence and their actual success rate—this skill helps researchers and developers distinguish signal from noise in a rapidly evolving technological landscape.
Características Principales
01Structured recording of predictions with author, timeframe, and confidence metadata
02Standardized JSON output for aggregating statistics across multiple predictors
03Precision accuracy scoring ranging from 0.0 to 1.0 based on empirical evidence
04Multi-state evaluation system including verified, falsified, and unfalsifiable statuses
05Detailed calibration analysis to identify overconfident or underconfident forecasters
060 GitHub stars
Casos de Uso
01Performing historical audits of past AI capabilities forecasts to improve future planning
02Building a track record database for AI industry influencers and researchers
03Identifying bias or goalpost-shifting in tech journalism and corporate roadmaps
