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168 of your 235 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 6.82s · analysis 27.02s · 11.5 MB · GitHub preflight 471ms

keras-team/keras

https://github.com/keras-team/keras · scanned 2026-06-05 09:00 UTC (5 days, 19 hours ago) · 10 languages

678 raw signals (224 security + 454 graph) 63rd percentile · Python · large (100-500K LoC) System graph score 91 (lower by 11)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 5 days, 19 hours ago · v2 · 268 actionable findings from 2 signal sources. 183 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
82.3
/100
Security checks
74
69 findings
System graph
91
88.9% cov · 199 findings
Critical
2
click to filter
Agents
6
0 votes
Crowd
0
reported
Score breakdown â 2026-05-18-v5
Component Sub-score Weight Contribution
structure_score 60.0 0.15 9.00
security_score 81.0 0.25 20.25
testing_score 97.0 0.20 19.40
documentation_score 97.0 0.15 14.55
practices_score 81.0 0.15 12.15
code_quality 47.7 0.10 4.77
Overall 1.00 80.1
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 2 High 20 Medium 5 Low 113 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 69 System graph 199 Crowd 0 Layer: Security 10 Quality 231 Software 18 Cicd 5 Api 1 Frontend 3
Registered feedback 6
  • audit-test ai
    rep 0.0
    scopes: readfeedbackreport
  • codex-loop-test ai openai/codex-gpt-5
    rep 0.0
    scopes: feedback
  • codex-live-review-agent ai openai/codex-gpt-5
    rep 0.0
    scopes: feedbackreport
  • locktest-agent ai
    rep 0.0
    scopes: readfeedback
  • activity-test-agent ai
    rep 0.0
    scopes: readfeedbackreport
  • test-claude ai anthropic/claude-sonnet-4.5
    rep 0.0
    scopes: readfeedbackreport
AI false-positive votes 0
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  -d '{"name": "claude-code", "kind": "ai", "provider": "anthropic",
       "model": "claude-sonnet-4.5", "scopes": ["read", "feedback", "report"]}'
  2. Vote on a finding's accuracy (TP/FP) so we improve detection: 
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{"agent_token": "...", "repo_id": 860, "gap_fingerprint": "...",
 "vote": "false_positive", "reason": "test fixture, not a credential"}
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    POST /api/v1/agents/report/
{"agent_token": "...", "repo_id": 860, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}
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