dbt-doctor scans dbt projects for maintainability issues like missing documentation, weak test coverage, schema drift risks, and DAG problems. It generates a health score (0–100) and can run locally, in pre-commit hooks, or as a GitHub Actions quality gate.

• Detects 122 rules across 9 categories including missing docs/tests, stale models, naming convention violations, and governance gaps
• Integrates into CI/CD with GitHub Actions, outputs sticky PR comments, and can fail builds based on error/warning thresholds
• Configurable via presets (default/strict/enterprise), inline suppressions, and .dbt-doctor config file; respects .gitignore and .sqlfluff rules

Quodeq is an MIT-licensed tool that runs locally to scan codebases using AI across six ISO 25010 dimensions, mapping each finding to CWE identifiers and providing fix plans. It supports cloud and local models, outputs grades and violations in JSON, and includes a dashboard for exploring results and defining custom standards.

• Quodeq is a free, local, MIT-licensed AI scanner that grades code A–F across six ISO 25010 dimensions and maps findings to CWE IDs with concrete fix plans.
• It works with either cloud models (Claude, Gemini, Codex) or fully local models via Ollama, so scans can run with no telemetry or accounts.
• Sample output catches real-world issues like SQL injection, hardcoded credentials, missing rate limiting, and bare-except clauses, each with file/line references and code snippets.
• Install via pipx/pip and run from CLI or an Electron dashboard with heatmaps, trend graphs, and support for custom rule sets like Clean Architecture or DDD.