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micheleg/dash-to-dock

https://github.com/micheleg/dash-to-dock · scanned 2026-06-05 14:29 UTC (5 days, 5 hours ago) · 10 languages

55 raw signals (7 security + 48 graph) 32nd percentile · Javascript · small (2-20K LoC) System graph score 96 (lower by 41)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 5 days, 5 hours ago · v2 · 28 actionable findings from 2 signal sources. 3 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
83.1
/100
Security checks
70
5 findings
System graph
96
66.7% cov · 23 findings
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 55.0 0.15 8.25
security_score 100.0 0.25 25.00
testing_score 0.0 0.20 0.00
documentation_score 46.0 0.15 6.90
practices_score 68.0 0.15 10.20
code_quality 50.0 0.10 5.00
Overall 1.00 55.4
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 0 High 0 Medium 3 Low 10 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 5 System graph 23 Crowd 0 Layer: Quality 13 Cicd 1 Software 5 Api 1 Frontend 7 Security 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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  -H "Content-Type: application/json" \
  -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": 1038, "gap_fingerprint": "...",
 "vote": "false_positive", "reason": "test fixture, not a credential"}
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    POST /api/v1/agents/report/
{"agent_token": "...", "repo_id": 1038, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}
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