What problem does it solve? Contributing to the ai-badger framework requires knowing its stack × feature catalog layout, worktree isolation rules, TDD test patterns, and a strict sequence of CI checks (pylint, index rebuild, version sync, schema validation, release guard). This Skill encodes that entire contribution workflow so changes pass CI on the first push instead of failing on stale indexes, unsynced plugin copies, or schema drift. ## Core Features & Use Cases - Catalog feature authoring: Step-by-step procedures for adding new agents (data-only via scaffolding.json), stacks, operational skills, hooks, and MCP server declarations under features/{stack}/{feature}/. - TDD and mock-framework test patterns: Reusable fixtures for building minimal mock framework trees, writing discriminating tests, and avoiding pitfalls like IsADirectoryError on templates or stale sys.modules caches. - CI gate checklist: Ordered commands for pylint, index_build.py --check, version_sync.py, changelog index, schema validation, release guard, and pytest, plus a reviewer quality gate for finished branches. - Use Case: When adding a new operational skill to ai-badger, follow the Skill to write the ADR, create tests first, implement the script with _bootstrap_lib(), author SKILL.md, rebuild the index, sync plugin copies, and run the full CI sequence before pushing. ## Quick Start Ask the agent to add a new operational skill to the ai-badger framework following the contribution workflow, starting with an ADR and failing tests.
