anthropics/skills

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70 of your 103 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 4.85s · analysis 6.22s · 10.3 MB · GitHub preflight 424ms

https://github.com/anthropics/skills · scanned 2026-06-05 04:46 UTC (11 hours, 28 minutes ago) · 10 languages

240 findings (96 legacy + 144 scanner) 28th percentile · Python · small (2-20K LoC) Scanner says 97 (lower by 38)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 11 hours, 28 minutes ago · v2 · 168 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`	40.0	0.15	6.00
`security_score`	52.9	0.25	13.22
`testing_score`	70.0	0.20	14.00
`documentation_score`	62.0	0.15	9.30
`practices_score`	70.0	0.15	10.50
`code_quality`	60.0	0.10	6.00
Overall		1.00	59.0

Severity distribution — click a segment to filter

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

Severity: Critical 1 High 66 Medium 23 Low 62 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 96 9-layer 72 Crowd 0 Layer: Software 41 Security 8 Quality 116 Api 1 Frontend 1 Cicd 1

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 97.1/100 with 77.8% coverage. It contains 670 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 72 findings — concentrated in quality (35), software (32), security (2). Risk profile is low: 0 critical, 0 high, 13 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

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