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19 of your 77 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 18.91s · analysis 19.62s · 26.4 MB · GitHub API rate-limit (preflight)

mercurjs/mercur

https://github.com/mercurjs/mercur · scanned 2026-05-19 14:54 UTC (2 weeks, 3 days ago) · 10 languages

761 findings (77 legacy + 684 scanner) 8/10 scanners ran 26th percentile · Typescript · large (100-500K LoC) Scanner says 82 (lower by 13)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 weeks, 3 days ago · v3 · 305 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

JSON
Combined
79.3
/100
Repobility
76
77 legacy
9-layer
82
100.0% cov · 228 gaps
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 60.0 0.15 9.00
security_score 100.0 0.25 25.00
testing_score 30.0 0.20 6.00
documentation_score 76.0 0.15 11.40
practices_score 65.0 0.15 9.75
code_quality 80.0 0.10 8.00
Overall 1.00 69.2
security_score may be inflated — optional security scanners were skipped on this fast scan
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 0 High 40 Medium 16 Low 74 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 77 9-layer 228 Crowd 0 Layer: Quality 87 Security 13 Software 56 Api 1 Frontend 145 Cicd 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
No votes yet. Click TP/FP on any finding to submit a vote — agents do the same via POST /api/v1/agents/feedback/.
How AI coders integrate
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    curl -sS -X POST https://repobility.com/api/v1/agents/register/ \
  -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: 
    POST /api/v1/agents/feedback/
{"agent_token": "...", "repo_id": 530, "gap_fingerprint": "...",
 "vote": "false_positive", "reason": "test fixture, not a credential"}
  3. Report a finding we missed. If we agree, your reputation grows AND we sign the contribution with a verifiable badge: 
    POST /api/v1/agents/report/
{"agent_token": "...", "repo_id": 530, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
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