garciapermanaa-cyber/absen-magang

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Scan timing: clone 1.64s · analysis 0.27s · 0.3 MB · GitHub preflight 442ms

https://github.com/garciapermanaa-cyber/absen-magang.git · scanned 2026-05-27 13:53 UTC (1 week, 2 days ago) · 10 languages

66 findings (18 legacy + 48 scanner) 10th percentile · Typescript · tiny (<2K LoC) Scanner says 79 (lower by 33)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 week, 2 days ago · v2 · 42 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`	55.0	0.15	8.25
`security_score`	90.9	0.25	22.73
`testing_score`	0.0	0.20	0.00
`documentation_score`	20.0	0.15	3.00
`practices_score`	30.0	0.15	4.50
`code_quality`	78.7	0.10	7.87
Overall		1.00	46.3

Severity distribution — click a segment to filter

Active filters: source: crowd × excluding tests × Reset all

Severity: Critical 0 High 8 Medium 3 Low 23 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 18 9-layer 24 Crowd 0 Layer: Security 4 Quality 15 Software 9 Frontend 2 Cicd 1 Api 11

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 79.2/100 with 77.8% coverage. It contains 115 nodes across 19 cross-layer flows, written primarily in mixed languages. Engine surfaced 24 findings — concentrated in api (11), software (9), frontend (2). Risk profile is low: 0 critical, 0 high, 2 medium. Recommended next step: open the api layer findings first — that's where the highest-impact wins live.

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