What problem does it solve? During multi-wave implementations, progress can be lost or untracked without a consistent way to save work, document remaining tasks, and verify code quality before moving on. ## Core Features & Use Cases - Safe Git Commits: Stages only relevant files (never git add -A), excludes secrets and build artifacts, and commits with a descriptive checkpoint message. - Progress Tracking: Creates or updates a TODO.md file with completed tasks, remaining work, and blockers. - Quality Verification: Runs Ruff lint and pytest inside Docker for Python backends, plus pnpm lint for TypeScript frontends, then reports any new errors. - Use Case: After finishing one wave of a multi-step feature implementation, run this Skill to commit the changes, update the task list, confirm tests pass, and get a summary of what remains. ## Quick Start Ask the AI to create a checkpoint of the current work, update the TODO list, and verify that lint and tests pass.