calibrate

name	calibrate
description	Score past predictions against actual outcomes. Closes the feedback loop.
tools	["mcp__wheat__wheat_add-claim","mcp__wheat__wheat_compile","mcp__wheat__wheat_search","mcp__wheat__wheat_status"]

/calibrate -- Score predictions vs outcomes

The user wants to check what actually happened after a sprint's recommendations were implemented.

Arguments

$ARGUMENTS

Expected format: /calibrate --outcome "what happened" or /calibrate <claim_id> "actual result"

Instructions

1. Parse the outcome: The user provides outcome data as free text or claim-specific results.

2. Match outcomes to predictions: Use wheat_search to find the original estimate, recommendation, or risk claims that predicted something. Compare prediction to actual outcome.

3. Create calibration claims as cal### claims with evidence tier production (these are real outcomes):

   • If prediction was accurate: factual claim noting the match
   • If prediction was wrong: factual claim noting the delta (predicted X, actual Y)
   • If prediction was partially right: estimate claim with the refined numbers

4. Compute accuracy scorecard:

   • Group by evidence tier: what % of stated vs web vs documented vs tested claims were accurate?
   • Group by claim type: are estimates less accurate than factual claims?
   • This validates whether the evidence tier system is predictive

5. Run wheat_compile.

6. Print scorecard:

   Calibration results:
   Predictions scored: <N>
   Accurate: <N> (<percent>)
   Partially accurate: <N>
   Wrong: <N>
   
   Accuracy by evidence tier:
     stated: <percent>
     web: <percent>
     documented: <percent>
     tested: <percent>
   
   Next steps:
     /brief              -- recompile with calibrated data
     /research <topic>   -- investigate where predictions went wrong