What problem does it solve? AI agents and developers generate code quickly, but without disciplined version control, changes become unreviewable, hard to revert, and risky to release. This Skill enforces atomic commits, short-lived branches, semantic versioning, and curated changelogs so every change stays manageable and every release communicates clearly to consumers. ## Core Features & Use Cases - Commit Discipline: Enforces atomic commits with conventional message types (feat, fix, refactor, test, docs, chore), pre-commit hygiene checks, and change summaries that document scope. - Branching & Parallel Work: Guides trunk-based development with short-lived feature branches, branch naming conventions, and git worktrees for running multiple AI agents in parallel. - Release & Versioning: Applies semantic versioning rules (MAJOR.MINOR.PATCH), annotated git tags as the source of truth, and human-readable changelogs grouped by Added/Changed/Fixed/Deprecated. - Use Case: An AI agent completes a feature across several commits, you tag v1.4.0, derive the version from the tag, and publish a changelog entry written alongside the change rather than reconstructed at release time. ## Quick Start Ask the agent to commit the current changes as atomic commits with conventional messages, then tag the release and write a changelog entry for the new version.