ring:linting-codebase

Automated lint remediation groups lint issues into independent streams and dispatches AI agents to apply minimal fixes concurrently. This parallel approach resolves thousands of lint errors across multiple files and directories in minutes.

Parallel lint analysis parses lint output to identify independent streams of issues, assigning one AI agent per stream to apply minimal changes concurrently. This stream-based approach enables concurrent fixes across files and directories.

The best way to handle thousands of lint errors is stream-based parallel remediation. It groups lint errors by file and dispatches multiple AI agents concurrently to achieve green lint in minutes.

Yes, AI agents apply safe, incremental edits by applying minimal changes to independent streams. The system enforces iterative verification, re-running lint after each batch of fixes until the codebase is lint-clean.

Run your project's standard lint command, such as npm run lint or make lint, to trigger the orchestration. Then monitor the generated streams and agent activity until all lint checks pass.

Automated parallel lint remediation applies only minimal, safe incremental edits and requires iterative verification by re-running lint after each batch. It is designed for large codebases with multiple languages and projects.