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utkarshalpha/multi-agent-research-pipeline

https://github.com/utkarshalpha/multi-agent-research-pipeline · scanned 2026-06-16 04:40 UTC (1 month, 3 weeks ago)

6 raw signals (0 security + 6 graph)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 month, 3 weeks ago · v1 · 6 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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77.8% cov · 6 findings

Severity distribution — click a segment to filter

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Severity: Critical 0 High 1 Medium 1 Low 3 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 0 System graph 6 Crowd 0 Layer: Hardware 1 Quality 4 Security 1

Exclude dismissed / FP Exclude test files

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Scan summary Repository scanned at 92.6/100 with 77.8% coverage. It contains 39 nodes across 5 cross-layer flows, written primarily in mixed languages. Engine surfaced 6 findings — concentrated in quality (4), hardware (1), security (1). Risk profile is high: 0 critical, 1 high, 1 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 5 of 6 actionable findings. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

high System graph security auth conf 1.00 FastAPI POST `research` without auth dependency — main.py:178

`@router.post` decorator with no `Depends(get_current_user)` or auth-shaped dependency in its signature. Mutating endpoints should require authentication unless explicitly public.

main.py:178 securityAuth fastapi unauth mutation

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

low System graph quality Production readiness conf 1.00 Composite production-readiness gap

Multiple low-cost hardening controls are missing together: license, ci. Opus verification showed these co-occurring gaps are a better readiness signal than reading each flag in isolation.

Repo hardeningGenerated repo pattern

low System graph hardware Supply chain conf 1.00 Docker base image is tag-pinned but not digest-pinned: python:3.12-slim

Container tags can be retagged upstream. Pin production base images to a reviewed digest (`image@sha256:...`) when reproducibility and supply-chain integrity matter.

Dockerfile:6 containersPinned dependencies

low System graph quality License conf 1.00 No license file detected

No LICENSE/COPYING/NOTICE file was found. Generated repositories often omit licensing, which blocks reuse and automated intake.

Repo hardeningGenerated repo pattern

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