What problem does it solve? It enforces a disciplined, evidence-based lifecycle for AI agent work in OpenCode, preventing unapproved edits, unvalidated changes, and self-confirmed completions by gating every phase behind explicit operator approval and objective evidence. ## Core Features & Use Cases - Lifecycle State Machine: Drives work through prepare, plan approval, run, validate, judge, refine, confirmation, and handoff states, with agents unable to transition states themselves. - Risk-Tiered Validation: Classifies work as low, medium, high, or untrusted risk (informed by the harness-risk-router) and scales review depth from auto to full code review with security analysis accordingly. - Parallel Lane Execution: Schedules approved plans as independent lanes via spec-lead, dispatching tracer-bullet tickets with declared file ownership sets in parallel. - Use Case: An operator approves a multi-file refactoring plan; the skill executes each lane in parallel, runs the quality gate in full mode, records the review, and stops at awaiting_confirmation with diff summary, evidence, and rollback notes before any handoff. ## Quick Start Ask the agent to prepare a PREVC plan for your change, then approve it and run the validated execution through to operator confirmation.