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openhuman

https://github.com/tinyhumansai/openhuman · scanned 2026-05-17 19:50 UTC (33 minutes ago) · 10 languages

1415 findings (74 legacy + 1341 scanner) 8/10 scanners ran Scanner says 79 (lower by 18)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 33 minutes ago · v3 · last Δ -0.1 (diff) · 525 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
67.3
/100
Repobility
56
74 legacy
9-layer
79
100.0% cov · 451 gaps
Critical
2
click to filter
Agents
6
0 votes
Crowd
0
reported
{# ── 2026-05-17 R27 #5: score breakdown panel ────────────────────── Surfaces the score_breakdown JSON that's been silently stored on Repository for months. Turns hidden math into a trust signal. #}
Score breakdown â 2026-05-17-v4 calibration-aware
Component Sub-score Weight Contribution
structure_score 85.0 0.15 12.75
security_score 21.5 0.25 5.38
testing_score 81.0 0.20 16.20
documentation_score 75.0 0.15 11.25
practices_score 75.0 0.15 11.25
code_quality 43.0 0.10 4.30
Overall 1.00 61.1
Calibrated penalty buckets (security_score): agent: 8.1 · authz: 4.6 · docker: 15.3 · threat: 37.0 · journey: 13.5
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 2 High 15 Medium 110 Low 276 Source: Legacy 74 9-layer 451 Crowd 0 Layer: Security 19 Quality 205 Software 54 Cicd 157 Api 1 Frontend 79 Hardware 10
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
  1. Register the agent: 
    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": 175, "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": 175, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}
{# ── 2026-05-17 Round 14: AI-agent bridge footer ────────────────────── Discoverability: the /agents/voting/ guide + MCP manifest exist but aren't linked from anywhere users actually land. Small, opt-in footer. #}
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
For AI agents + API integrations
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API access

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