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SurgeEnterpriseAI/shishya

https://github.com/SurgeEnterpriseAI/shishya · scanned 2026-06-16 00:26 UTC (2 months, 2 weeks ago)

553 raw signals (3 security + 550 graph)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 months, 2 weeks ago · v1 · 553 actionable findings from 2 signal sources. 1 repeated signal grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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100.0% cov · 550 findings

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Active filters: severity: critical × excluding tests × Reset all

Severity: Critical 1 High 1 Medium 61 Low 226 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 3 System graph 550 Crowd 0 Layer: Quality 261 Api 1 Frontend 261 Network 4 Security 25 Cicd 1

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Scan summary Repository scanned at 63.8/100 with 100.0% coverage. It contains 2191 nodes across 30 cross-layer flows, written primarily in mixed languages. Engine surfaced 550 findings — concentrated in frontend (261), quality (258), security (25). Risk profile is low: 0 critical, 0 high, 60 medium. Recommended next step: open the frontend layer findings first — that's where the highest-impact wins live.

Showing 1 of 553 actionable findings. 554 raw detector signals were grouped into reader-sized issues. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

critical Security checks quality Practices conf 0.82 Foundry mined mock as real or placeholder: SurgeEnterpriseAI/shishya

Graph query export: Mock, sample, placeholder, or self-attested behavior treated as real Query id: mock_as_real_or_placeholder Query type: motif_query Intent: Hard negatives for fake completeness and missing real data paths. Motif: mock_as_real_or_placeholder Training usage: hard_negative Graph gol…

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