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abhmul/ask-chatgpt

https://github.com/abhmul/ask-chatgpt · scanned 2026-06-17 01:28 UTC (1 month, 2 weeks ago)

52 raw signals (0 security + 52 graph)

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 month, 2 weeks ago · v2 · last Δ +18.5 (diff) · 44 actionable findings from 1 signal source. 8 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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Active filters: severity: medium × excluding tests × Reset all
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Scan summary Repository scanned at 95.2/100 with 44.4% coverage. It contains 1316 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 52 findings — concentrated in software (29), quality (23). Risk profile is low: 0 critical, 0 high, 2 medium. Recommended next step: open the software layer findings first — that's where the highest-impact wins live.

Showing 2 of 44 actionable findings. 52 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.

medium System graph quality Placeholder conf 1.00 Critical user flow still appears backed by mock or placeholder data
A payment/auth/admin/order/billing-style flow contains mock, fake, TODO, dummy, or placeholder markers in runtime source. In the Fable corpus this is a high-leverage completeness smell: the app can look finished while the money, identity, or tenant flow is still scaffolded.
Mock dataCritical flowGenerated repo pattern
medium System graph quality Placeholder conf 1.00 Placeholder or mock-heavy implementation detected
Found 30 placeholder/mock markers across 8 source files. This often means the repo looks complete while core flows still use generated scaffolding or fake data.
Mock dataIncompleteGenerated repo pattern
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