re-scoring
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npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/re-scoringRe-evaluates S/O/D scores after mitigation to validate risk reduction
- Validates whether implemented mitigations effectively reduce risk
- Uses subagent execution to avoid bias from original scores
- Compares new scores against original to assess mitigation effectiveness
- Returns updated scores, effectiveness metrics, and high-risk modes
SKILL.md
.github/skills/re-scoringView on GitHub ↗
--- name: re-scoring description: Re-evaluate S/O/D scores after mitigation measures are in place. Validates that mitigations actually reduce risk as expected. execution: subagent prompt: ./prompt.md input: failure_modes (string), mitigations (string), original_scores (string) used-by: [failure-anticipation] --- # Re-Scoring Re-evaluates Severity, Occurrence, and Detection scores after mitigations are applied. ## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Why Subagent Re-scoring requires fresh evaluation without anchoring to original scores. Isolated context prevents confirmation bias toward expected improvement. ## Input - **failure_modes**: Original failure mode descriptions - **mitigations**: Proposed mitigation measures - **original_scores**: Pre-mitigation S/O/D scores for comparison ## Output - **new_scores**: Post-mitigation S, O, D, and RPN for each mode - **effectiveness**: Comparison with original scores - **still_high**: Modes that remain H-priority after mitigation
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