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pytorch/ao

https://github.com/pytorch/ao · scanned 2026-06-16 04:36 UTC (1 month, 3 weeks ago)

7 raw signals (0 security + 7 graph)

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Complete repo analysis

Last scanned 1 month, 3 weeks ago · v1 · 7 actionable findings from 1 signal source. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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44.4% cov · 7 findings

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 0 Medium 4 Low 2 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 0 System graph 7 Crowd 0 Layer: Security 1 Quality 6

Exclude dismissed / FP Exclude test files

Scan summary Repository scanned at 74.8/100 with 44.4% coverage. It contains 40 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 7 findings — concentrated in quality (6), security (1). Risk profile is low: 0 critical, 0 high, 4 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 6 of 7 actionable findings. 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 Agent instructions conf 1.00 Agent authority lacks a verifier contract: CLAUDE.md

This agent instruction grants code or shell authority but does not state the verification gate that decides promotion. The recurring safe pattern is: LLM proposes; deterministic tests/build/security checks verify; only verified code promotes.

CLAUDE.md VerificationClaude instruction

medium System graph quality Agent instructions conf 1.00 Agent instructions exist but release-hardening basics are missing

AI-coder instruction files were found, but the repo is missing ci. Treat this as a contract gap: the agent is guided, but the generated output is not yet guarded by the controls that make it repeatable.

Repo hardeningGenerated repo pattern

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 security Coverage conf 1.00 No auth library detected

The scanner did not find any standard auth library (JWT, OAuth, NextAuth, Auth0, etc.). The repo has auth/admin/session surface indicators, so auth may live in custom code, in a separate service, or be missing.

auth

low System graph quality Debug conf 1.00 Debug logging residue appears in source files

Found 29 console/debugger/print-style debug statements in non-test source. This is a common fast-generation residue before production cleanup.

CleanupRepo hardeningGenerated repo pattern

low System graph quality Integrity conf 1.00 Old/deprecated-named symbol `detect_hipify_v2` in setup.py:185

Names with suffixes like `_old`, `_v1`, `_deprecated` usually indicate replaced-but-not-removed code (typical AI-coder leftover). Confirm and delete, or rename if it's the active version.

old markerDead code

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