jcode

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https://github.com/1jehuang/jcode.git · scanned 2026-05-16 12:49 UTC (1 day, 9 hours ago) · 10 languages

102 findings (14 legacy + 88 scanner) 2/10 scanners ran 40th percentile · Rust · large (100-500K LoC) Scanner says 85 (lower by 15)

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Complete repo analysis

Last scanned 1 week ago · v4 · 100 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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{# ── 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`	80.4	0.25	20.10
`testing_score`	36.0	0.20	7.20
`documentation_score`	86.0	0.15	12.90
`practices_score`	65.0	0.15	9.75
`code_quality`	70.0	0.10	7.00
Overall		1.00	69.7

Calibrated penalty buckets (security_score): threat: 19.6

security_score may be inflated — optional scanners skipped due to repo size/fast scan

Severity distribution — click a segment to filter

Active filters: source: crowd × excluding tests × Reset all

Scan summary Repository scanned at 84.6/100 with 100.0% coverage. It contains 1723 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 88 findings — concentrated in quality (66), security (15), software (5). Risk profile is high: 12 critical, 0 high, 10 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

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{# ── 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.

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