◇ Could this help me?
Yes, for an advanced user with guardrails in place first. The accountability design is the adoptable substance and is genuinely good engineering: a fresh inspector instance grades every task, pass/fail report cards build a per-task trust record, autonomy is earned at a 95% success rate over 20 runs and revoked below 90% with an alert, and daily regression checks catch silent breakage. Prerequisites before any unattended operation: a sandboxed environment, tightly scoped permissions, hard cost caps, everything under version control, and post-run review of what changed; without those, run the same mechanisms inside supervised sessions. This is project-specific infrastructure to build deliberately, not a general Claude Code setting.
Design a supervised accountability layer for an existing AI-assisted workflow, borrowing the good parts of autonomous-loop designs without unattended execution: after each task, have a fresh model instance inspect the result and log pass or fail to a simple report card grouped by task type; track the success rate per task type over the last 20 runs; propose which steps have earned lighter review and which need closer review when their grade drops; and maintain a small set of regression checks that rerun at the start of each session to flag anything that quietly broke. All execution stays inside supervised sessions with explicit approval before changes land. Propose the simplest file layout for the report card and regression checks.