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Scan timing: clone 2.74s · analysis 5.97s · 2.4 MB · GitHub preflight 594ms

kalynnka/octomate

https://github.com/kalynnka/octomate.git · scanned 2026-06-25 17:45 UTC (1 month ago) · 10 languages

249 raw signals (105 security + 144 graph) 18th percentile · Python · medium (20-100K LoC)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 month ago · v1 · 176 actionable findings from 2 signal sources. 73 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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77.8% cov · 114 findings

Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	75.0	0.15	11.25
`security_score`	24.3	0.25	6.08
`testing_score`	100.0	0.20	20.00
`documentation_score`	58.0	0.15	8.70
`practices_score`	52.0	0.15	7.80
`code_quality`	60.9	0.10	6.09
Overall		1.00	59.9

Severity distribution — click a segment to filter

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Severity: Critical 1 High 40 Medium 47 Low 65 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 62 System graph 114 Crowd 0 Layer: Quality 64 Security 57 Software 40 Cicd 10 Network 3 Hardware 2

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Scan summary Repository scanned at 53.6/100 with 77.8% coverage. It contains 1634 nodes across 6 cross-layer flows, written primarily in mixed languages. Engine surfaced 144 findings — concentrated in security (69), quality (57), software (13). Risk profile is high: 0 critical, 33 high, 34 medium. Recommended next step: open the security layer findings first — that's where the highest-impact wins live.

Showing 0 of 176 actionable findings. 249 raw detector signals were grouped into reader-sized issues. 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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