process-feedback

To turn model transcript failures into durable skill improvements, you analyze failing transcripts to extract generalized skill update proposals, preventing recurrence without leaking project-specific details. This preserves evidence sequences and maps misconceptions to instruction-shape taxonomy constraints for safe maintenance updates.

Root-cause analysis for instruction tuning failures identifies why a model fails to follow skill guidance by examining phase ordering, proof verification, recovery behavior, and instruction-shape constraints. It separates claims from proof in transcripts to pinpoint the exact mechanic requiring a correction.

You perform feedback triage on failing AI model transcripts by preserving the evidence sequence, separating claims from proof, and mapping the identified misconception to a generalized wording fix. This workflow drafts targeted edits and recommends eval updates for reproducible failures.

Yes, you can use read-only git exploration for skill maintenance updates. The workflow requires read-only repository exploration to audit nearby skills for wording leakage and posture drift, ensuring generalized fixes are drafted safely without modifying project-specific details during analysis.

You design eval updates for reproducible model failures by converting correction threads into targeted test cases that prevent recurrence. The process drafts eval changes that specifically address the identified instruction-shape constraints and misconception taxonomy failures found in the transcript.

The limitation of using generalized wording for skill maintenance is that it strictly avoids leaking project-specific details from the original transcripts. This means fixes are constrained to broad instruction-shape adjustments and taxonomy mapping, potentially missing highly contextual edge cases.