Review docs/ai/testing/feature-{name}.md and ensure it mirrors the base template before writing tests.

1. Gather Context — If not already provided, ask for: feature name/branch, summary of changes (link to design & requirements docs), target environment, existing test suites, and any flaky/slow tests to avoid.
2. Use Memory for Context — Search memory for existing testing patterns and prior edge cases: npx ai-devkit@latest memory search --query "<feature testing strategy>".
3. Analyze Testing Template — Identify required sections from docs/ai/testing/feature-{name}.md. Confirm success criteria and edge cases from requirements & design docs. Note available mocks/stubs/fixtures.
4. Unit Tests (aim for 100% coverage) — For each module/function: list behavior scenarios (happy path, edge cases, error handling), generate test cases with assertions using existing utilities/mocks, and highlight missing branches preventing full coverage.
5. Integration Tests — Identify critical cross-component flows. Define setup/teardown steps and test cases for interaction boundaries, data contracts, and failure modes.
6. Coverage Strategy — Recommend coverage tooling commands. Call out files/functions still needing coverage and suggest additional tests if <100%.
7. Store Reusable Knowledge — Save reusable testing patterns or tricky fixtures with npx ai-devkit@latest memory store ....
8. Update Documentation — Summarize tests added or still missing. Update docs/ai/testing/feature-{name}.md with links to test files and results. Flag deferred tests as follow-up tasks.
9. Next Command Guidance — If tests expose design issues, return to /review-design; otherwise continue to /code-review.