What problem does it solve? Writing or fixing tests in an unfamiliar repository often means guessing at naming conventions, mock patterns, and assertion styles, which produces inconsistent tests that fail code review. ## Core Features & Use Cases - Stored Learnings Retrieval: Loads repo-specific test pattern learnings from a wiki collection or flat learning files depending on the repository type. - Live Test Scan: Scans nearby test files to capture naming conventions, import patterns, mock strategies, fixture factories, and assertion styles. - Structured Context Output: Writes a structured markdown context block to a timestamped file so calling skills like write-unit-tests or cleanup-e2e-tests can read the exact conventions. - Use Case: Before generating a new unit test, a parent skill invokes this sub-skill to gather how the repo mocks stores, builds test data, and handles async assertions, ensuring the generated test matches existing style. ## Quick Start Ask the AI to gather the test conventions and mock patterns for this repository before writing new unit tests.
