antipattern-detect

Automated antipattern detection collects failure data from linters like ruff and eslint, classifies patterns by category, deduplicates entries, and appends findings to a markdown knowledge base. It surfaces recurring issues from test runners and code reviews for team-wide visibility.

Centralizing code review feedback involves classifying antipatterns into categories like security and performance, deduplicating with existing entries, and documenting findings in a markdown knowledge base. This provides asynchronous updates and team-wide visibility for learning.

Yes, automated antipattern detection enforces non-blocking operation during development. It provides inline feedback synchronously while performing asynchronous writes to update the knowledge base, ensuring your workflow is not interrupted by documentation tasks.

Antipattern detection works with pytest, golangci-lint, and other tools like vitest and terratest. It collects failure data from these test runners and linters to identify recurring issues and document them in your knowledge base.

Deduplicating antipattern findings involves comparing newly classified patterns from linting and test failures against existing knowledge base entries. This prevents redundant documentation and maintains a clean, centralized record of recurring issues.