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Abhiraam2403/titanic-data-analysis

https://github.com/Abhiraam2403/titanic-data-analysis.git · scanned 2026-05-28 14:33 UTC (1 week, 1 day ago) · 10 languages

10 findings (2 legacy + 8 scanner) 22nd percentile · Unknown · Scanner says 85 (lower by 20)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 week, 1 day ago · v2 · 6 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

JSON
Combined
74.9
/100
Repobility
65
2 legacy
9-layer
85
55.6% cov · 4 gaps
Critical
0
click to filter
Agents
6
0 votes
Crowd
0
reported
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 20.0 0.15 3.00
practices_score 70.0 0.15 10.50
code_quality 80.0 0.10 8.00
Overall 1.00 65.0
Severity distribution — click a segment to filter
Active filters: layer: api × excluding tests × Reset all
Severity: Critical 0 High 0 Medium 3 Low 1 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 2 9-layer 4 Crowd 0 Layer: Quality 2 Api 1 Frontend 1 Security 1 Cicd 1
Scan summary Repository scanned at 84.8/100 with 55.6% coverage. It contains 2 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 4 findings — concentrated in api (1), frontend (1), security (1). 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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