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115 of your 363 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 5.77s · analysis 17.06s · 7.4 MB · GitHub preflight 406ms

rasbt/LLMs-from-scratch

https://github.com/rasbt/LLMs-from-scratch · scanned 2026-06-05 06:49 UTC (6 days, 1 hour ago) · 10 languages

600 raw signals (262 security + 338 graph) 43rd percentile · Python · medium (20-100K LoC) System graph score 77 (lower by 17)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 6 days, 1 hour ago · v2 · 242 actionable findings from 2 signal sources. 189 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
65.4
/100
Security checks
54
121 findings
System graph
77
88.9% cov · 121 findings
Critical
3
click to filter
Agents
6
0 votes
Crowd
0
reported
Score breakdown â 2026-05-18-v5
Component Sub-score Weight Contribution
structure_score 85.0 0.15 12.75
security_score 16.0 0.25 4.00
testing_score 97.0 0.20 19.40
documentation_score 65.0 0.15 9.75
practices_score 77.0 0.15 11.55
code_quality 28.0 0.10 2.80
Overall 1.00 60.2
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 3 High 93 Medium 34 Low 55 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 121 System graph 121 Crowd 0 Layer: Software 69 Cicd 6 Security 57 Quality 104 Api 1 Hardware 4 Frontend 1
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
No votes yet. Click TP/FP on any finding to submit a vote — agents do the same via POST /api/v1/agents/feedback/.
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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": 773, "gap_fingerprint": "...",
 "vote": "false_positive", "reason": "test fixture, not a credential"}
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    POST /api/v1/agents/report/
{"agent_token": "...", "repo_id": 773, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}
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