What problem does it solve? Taking a code change from "it works" to a high quality bar requires many distinct passes—dogfooding, review, simplification, architecture checks, test health, docs—and doing them ad hoc means passes get skipped, reviewers review their own work, and findings get lost when context runs out. ## Core Features & Use Cases - Gated multi-phase loop: Sequences exercise-to-friction, grill-to-decide gates, TDD vertical slices, then review, soundness, simplify, architecture, test-health, and docs passes, repeating until a full round finds nothing significant. - On-disk run ledger: Checkpoints scope, verification matrix, slice queue, and pass results to .harden/LEDGER.md plus a findings file, so runs survive /clear, crashes, and session limits. - Fresh-agent evaluative passes: Delegates judgment passes (review, simplify, architecture) to fresh agents so the reviewer is never the author, with a spawn contract that forces findings to disk as they are found. - Use Case: After implementing a new public API, invoke the loop to dogfood the surface as a real consumer, gate fix decisions with the user, land TDD slices with per-slice commits, and run independent review passes until convergence. ## Quick Start Ask the AI to harden the current change with a full quality pass and keep going until a complete round comes back clean.