siddheshbhurke / attend-ai
Code health
siddheshbhurke/attend-ai scores 7.2 out of 10 on defect risk, which Repowise rates Good. Maintainability scores 9.3 out of 10. Static performance risk scores 9.8 out of 10. The three are scored separately and never blended into a single number. 5 of 23 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 23
- Symbols
- 55
- Modules
- 1
- Doc pages
- 32
- Dead exports
- 0
Where the risk concentrates
All 5 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 |
|---|---|---|---|---|
| teacher_screen.pysrc/screens | 95.5th %ile | 0 | 1bus factor 1 | 9 |
| base_layout.pysrc/ui | 90.9th %ile | 0 | 1bus factor 1 | 5 |
| student_screen.pysrc/screens |
Read more about siddheshbhurke/attend-ai
- 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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Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/siddheshbhurke/attend-ai)How siddheshbhurke/attend-ai works
This page is an auto-generated, always-fresh map of the siddheshbhurke/attend-ai 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 23 source files, 55 symbols, and 2 languages, organised into 1 modules, led by python, markdown. Static analysis leaves 40 open findings across the three health dimensions.
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