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repowiseabdo12km/autocompile

abdo12km / autocompile

Tool-free LLVM pass-sequence prediction with fine-tuned language models, supervised search labels, and compiler-based evaluation.

LLVM1github.com/abdo12km/autocompile
Indexed atd1c799b·12h ago·Up to date with upstream
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Code health

8.6out of 10Excellent

abdo12km/autocompile scores 8.6 out of 10 on defect risk, which Repowise rates Excellent. Maintainability scores 9.2 out of 10. Static performance risk scores 9.6 out of 10. The three are scored separately and never blended into a single number. 9 of 125 files are git hotspots, the places where changes and bug fixes concentrate.

Full health report
Defect risk8.6/10 · Excellent
Maintainability9.2/10 · Excellent
Performance risk9.6/10 · Excellent
Files
125
Symbols
601
Modules
7
Doc pages
104
Dead exports
0

Where the risk concentrates

All 9 hotspots

Ranked by prior bug fixes and change frequency, mined from the full git history rather than from the code alone.

FileChurnPrior fixesMaintainersCommits 90d
test_smoke.pytests98.3th %ile5bug magnet3bus factor 115
eval_search_sft.pyscripts/training96.6th %ile32bus factor 19

Read more about abdo12km/autocompile

  • 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.

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How abdo12km/autocompile works

Tool-free LLVM pass-sequence prediction with fine-tuned language models, supervised search labels, and compiler-based evaluation. This page is an auto-generated, always-fresh map of the abdo12km/autocompile repository, written primarily in LLVM. 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 125 source files, 601 symbols, and 5 languages, organised into 7 modules, led by python, markdown, json. Static analysis leaves 241 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.

train_sft.pyscripts/training
94.9th %ile
1
2bus factor 1
8
eval_agent_sft.pyscripts/training91.5th %ile12bus factor 16
test_agent_masking.pytests88.1th %ile124
convert_search_labels_to_sft.pyscripts/training93.2th %ile02bus factor 15
  • CommitsChange-risk ranked history with AI-agent provenance and hotspots
  • ContributorsBus-factor, per-file maintainer maps, and human/agent collaboration
  • DecisionsArchitectural decisions extracted from commits and PRs
  • Chat with this repoSign inProAsk grounded questions over the indexed code