prefecthq / prefect
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
Code health
prefecthq/prefect scores 8.1 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 8.3 out of 10. Static performance risk scores 9.9 out of 10. The three are scored separately and never blended into a single number. 73 of 4,839 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 4.8k
- Symbols
- 36k
- Modules
- 165
- Doc pages
- 2.5k
- Dead exports
- 219
Where the risk concentrates
All 73 hotspotsRanked by prior bug fixes and change frequency, mined from the full git history rather than from the code alone.
| File | Churn | Prior fixes | Maintainers | Commits 90d |
|---|---|---|---|---|
| test_base_worker.pytests/workers | 100.0th %ile | 0 | 4 | 13 |
| test_flow_engine.pytests | 99.9th %ile | 0 | 3 | 7 |
| dashboard.tsxui-v2/src/routes | 99.9th %ile |
Read more about prefecthq/prefect
- OverviewFiles, symbols, languages, packages, git intelligence
- ArchitectureDependency graph, layered architecture, symbol index, and third-party dependencies
- Code healthPer-file health scores, hotspots, test coverage, dead code, and refactoring targets
- AI docsProModule-by-module documentation generated from source
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Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/prefecthq/prefect)How prefecthq/prefect works
Prefect is a workflow orchestration framework for building resilient data pipelines in Python. This page is an auto-generated, always-fresh map of the prefecthq/prefect repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, scores per-file code health from complexity, duplication, test coverage and churn, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.
The codebase has 4,839 source files, 36,096 symbols, and 12 languages, organised into 165 modules, led by python, typescript, markdown. Static analysis leaves 10,993 open findings across the three health dimensions.
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