rnwolf / ccpm-scheduler
Deterministic Critical Chain Project Management (CCPM) scheduler — Python library + CLI for humans and AI agents
Code health
rnwolf/ccpm-scheduler scores 6.2 out of 10 on defect risk, which Repowise rates Fair. Maintainability scores 8.4 out of 10. Static performance risk scores 10.0 out of 10. The three are scored separately and never blended into a single number. 4 of 31 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 31
- Symbols
- 134
- Modules
- 3
- Doc pages
- 25
- Dead exports
- 0
Where the risk concentrates
All 4 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 |
|---|---|---|---|---|
| build.pysrc/ccpm_scheduler | 94.7th %ile | 0 | 1bus factor 1 | 9 |
| check.pysrc/ccpm_scheduler | 89.5th %ile | 0 | 1bus factor 1 | 5 |
| test_api.pytests |
Read more about rnwolf/ccpm-scheduler
- 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 explanationsProModule-by-module documentation generated from source
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[](https://repowise.dev/repo/rnwolf/ccpm-scheduler)How rnwolf/ccpm-scheduler works
Deterministic Critical Chain Project Management (CCPM) scheduler — Python library + CLI for humans and AI agents This page is an auto-generated, always-fresh map of the rnwolf/ccpm-scheduler 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 31 source files, 134 symbols, and 5 languages, organised into 3 modules, led by markdown, python, yaml. Static analysis leaves 113 open findings across the three health dimensions.
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