What problem does it solve? After an AI-assisted implementation finishes, it is hard to know whether every planned task was actually completed, whether the code compiles and passes tests, and whether the work still respects the project's architecture, rules, and roadmap. This Skill performs a structured post-implementation audit so gaps are caught before commit or merge. ## Core Features & Use Cases - Task Completion Audit: Walks every task in the plan file and marks each as complete, partial, not found, or skipped by inspecting the actual code with Glob and Grep. - Quality Checks: Runs build, test, and lint commands matched to the detected stack (TypeScript, Go, Python, Rust, PHP) and checks dependencies, leftover TODO markers, and undocumented environment variables. - Context Gates: Evaluates architecture, rules, and roadmap alignment with normal and strict modes, then appends a machine-readable aif-gate-result JSON block for orchestrators. - Use Case: After running /aif-implement on a feature branch, invoke verification to confirm all 8 planned tasks exist in code, tests pass, and no undocumented SENDGRID_API_KEY-style config slipped in before committing. ## Quick Start Ask the assistant to verify the completed implementation against the current plan, optionally in strict mode before merging.