VILA-Lab/Dive-into-Claude-Code

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https://github.com/VILA-Lab/Dive-into-Claude-Code.git · scanned 2026-05-16 02:07 UTC (2 weeks, 6 days ago) · 10 languages

5 findings (2 legacy + 3 scanner) 16th percentile · Unknown · Scanner says 93 (lower by 36)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 weeks, 6 days ago · v1 · 1 finding from 1 source. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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Score breakdown â 2026-05-14-v3

Component	Sub-score	Weight	Contribution
`structure_score`	45.0	0.15	6.75
`security_score`	100.0	0.25	25.00
`testing_score`	0.0	0.20	0.00
`documentation_score`	60.0	0.15	9.00
`practices_score`	55.0	0.15	8.25
`code_quality`	80.0	0.10	8.00
Overall		1.00	57.0

Severity distribution — click a segment to filter

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Severity: Critical 0 High 0 Medium 1 Low 0 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 1 9-layer 0 Crowd 0 Layer: Quality 1

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Scan summary Repository scanned at 92.9/100 with 55.6% coverage. It contains 15 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 0 findings. Risk profile is low: 0 critical, 0 high, 0 medium. Recommended next step: open the software layer findings first — that's where the highest-impact wins live.

Showing 1 of 1 findings. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

medium Legacy quality practices conf 1.00 [CFG006] Missing .gitignore: No .gitignore file. Risk of committing secrets and build artifacts.

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practiceslegacy

For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.

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