ADG-AUDISIS/OCR-IA

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Scan timing: clone 1.81s · analysis 0.07s · 0.2 MB · GitHub preflight 515ms

https://github.com/ADG-AUDISIS/OCR-IA.git · scanned 2026-05-27 21:20 UTC (1 week, 1 day ago) · 10 languages

48 findings (28 legacy + 20 scanner) 57th percentile · Python · tiny (<2K LoC) Scanner says 90 (lower by 22)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 week, 1 day ago · v2 · 38 findings from 2 sources. 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 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	30.0	0.15	4.50
`security_score`	100.0	0.25	25.00
`testing_score`	70.0	0.20	14.00
`documentation_score`	60.0	0.15	9.00
`practices_score`	70.0	0.15	10.50
`code_quality`	48.0	0.10	4.80
Overall		1.00	67.8

Severity distribution — click a segment to filter

Active filters: layer: data × excluding tests × Reset all

Severity: Critical 0 High 26 Medium 6 Low 3 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 28 9-layer 10 Crowd 0 Layer: Quality 31 Software 3 Api 1 Frontend 1 Security 1 Cicd 1

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 89.8/100 with 77.8% coverage. It contains 29 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 10 findings — concentrated in software (3), quality (3), api (1). Risk profile is low: 0 critical, 0 high, 4 medium. Recommended next step: open the software layer findings first — that's where the highest-impact wins live.

Showing 0 of 38 findings. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

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