opentraces-skill-verifier

To build and calibrate verifiers for AI skill alignment, you author and calibrate rubrics focusing on evidence and human oversight. You can run 'opentraces skill verifier creator init' to design rubrics and label traces for precise evaluation.

Skill autoverification is a mechanism where an AI agent self-aligns and judges its own performance against calibrated rubrics. It evaluates performance by ensuring rubrics only reward outputs when proper discrimination and evidence are demonstrated.

Yes, you can manually label traces to calibrate rubrics for better discrimination. The skill supports a manual alignment session mode, allowing you to apply human oversight to evidence-based rubric development and system calibration.

The best way to ensure rubrics only reward outputs with proper discrimination is to use a verifier calibration process that emphasizes evidence and human oversight. This approach validates security and trust during skill alignment and autoverification.

Autoverification requires human oversight during agent alignment to ensure security and trust. Human oversight validates that the calibrated rubrics maintain proper discrimination and accurately evaluate evidence before promoting skill rubrics.