bmsull560 / fabric_4l
Enterprise agentic SaaS platform — transform unstructured data into structured knowledge through ontology-guided AI pipelines
Code health
bmsull560/fabric_4l scores 8.5 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 9.2 out of 10. Static performance risk scores 9.9 out of 10. The three are scored separately and never blended into a single number. 841 of 7,067 files are git hotspots, the places where changes and bug fixes concentrate.
Full health report- Files
- 7.1k
- Symbols
- 44k
- Modules
- 78
- Doc pages
- 6.2k
- Dead exports
- 412
Where the risk concentrates
All 841 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 |
|---|---|---|---|---|
| main.pyservices/layer2-extraction/src/layer2_extraction/api | 100.0th %ile | 18bug magnet | 5bus factor 1 | 71 |
| middleware.pypackages/shared/src/value_fabric/shared/identity | 99.9th %ile | 15bug magnet | 7 |
Read more about bmsull560/fabric_4l
- 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/bmsull560/fabric_4l)Code health badge
Average health across every file, straight from the latest index.
[](https://repowise.dev/repo/bmsull560/fabric_4l)How bmsull560/fabric_4l works
Enterprise agentic SaaS platform — transform unstructured data into structured knowledge through ontology-guided AI pipelines This page is an auto-generated, always-fresh map of the bmsull560/fabric_4l 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 7,067 source files, 44,137 symbols, and 14 languages, organised into 78 modules, led by python, markdown, typescript. Static analysis leaves 13,236 open findings across the three health dimensions.
Use the links above to open the interactive dashboards, or connect this repo to your editor via the Repowise MCP server for grounded answers inside Claude, Cursor, or VS Code.