shanto5051/fraudwatch

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https://github.com/shanto5051/fraudwatch.git · scanned 2026-05-28 14:43 UTC (1 week, 1 day ago) · 10 languages

23 findings (11 legacy + 12 scanner) 20th percentile · Typescript · tiny (<2K LoC) Scanner says 96 (lower by 42)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 week, 1 day ago · v2 · 17 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`	60.0	0.15	9.00
`security_score`	95.9	0.25	23.98
`testing_score`	0.0	0.20	0.00
`documentation_score`	44.1	0.15	6.62
`practices_score`	42.0	0.15	6.30
`code_quality`	80.0	0.10	8.00
Overall		1.00	53.9

Severity distribution — click a segment to filter

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

Severity: Critical 0 High 0 Medium 5 Low 7 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 11 9-layer 6 Crowd 0 Layer: Quality 9 Security 2 Software 3 Api 1 Frontend 1 Cicd 1

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 96.0/100 with 100.0% coverage. It contains 34 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 6 findings — concentrated in software (2), api (1), frontend (1). Risk profile is low: 0 critical, 0 high, 2 medium. Recommended next step: open the software layer findings first — that's where the highest-impact wins live.

Showing 0 of 17 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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