- Enterprise SAML/OIDC callbacks used raw 'sso_xxx' string as passwordHash which would crash bcrypt.compare(). Now uses userRepo.hashPassword(randomUUID()) - Added updateLastLogin() for existing enterprise SSO users - Upgraded bcrypt cost factor from 10 to 12 per PRD spec - All 53 auth tests passing
91 lines
3.4 KiB
Markdown
91 lines
3.4 KiB
Markdown
---
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description: Advanced code review for PRs across ByteLyst workspace repos
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---
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# Advanced Code Review
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Your task is to find all potential bugs and code improvements in the code
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changes. Focus on:
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1. Logic errors and incorrect behavior
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2. Edge cases that aren't handled
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3. Null/undefined reference issues
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4. Race conditions or concurrency issues
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5. Security vulnerabilities
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6. Improper resource management or resource leaks
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7. API contract violations
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8. Incorrect caching behavior, including cache staleness issues, cache
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key-related bugs, incorrect cache invalidation, and ineffective caching
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9. Violations of existing code patterns or conventions
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10. Duplicate code
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Make sure to:
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1. If you find any pre-existing bugs in the code, you should also report those
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since it's important for us maintain general code quality for the user.
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2. Do NOT report issues that are speculative or low-confidence. All your
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conclusions should be based on a complete understanding of the codebase.
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3. Remember that if you were given a specific git commit, it may not be checked
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out and local code states may be different.
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## Scope
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All code across the ByteLyst workspace repos:
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- **learning_ai_common_plat** - Shared platform packages and services
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- packages/ - @bytelyst/\* shared libraries
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- services/ - platform-service, extraction-service
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- dashboards/ - admin-web, tracker-web
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- **Product repos** - Individual product backends and applications
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- learning_voice_ai_agent (LysnrAI)
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- learning_multimodal_memory_agents (MindLyst)
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- learning_ai_clock (ChronoMind)
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- learning_ai_fastgap (NomGap)
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- learning_ai_flowmonk (FlowMonk)
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- learning_ai_jarvis_jr (JarvisJr)
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- learning_ai_peakpulse (PeakPulse)
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- learning_ai_notes (NoteLett)
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- learning_ai_trails (ActionTrail)
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- learning_ai_smart_auth (SmartAuth)
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- learning_ai_auth_app (ByteLyst Auth)
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- learning_ai_productivity_web (Productivity Tools)
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## Domain Context
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This is a multi-product ecosystem with shared platform services. Key architectural patterns:
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- **Platform services** (Fastify 5, TypeScript ESM) provide auth, telemetry, feature flags, etc.
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- **Shared packages** (@bytelyst/\*) eliminate duplication across products
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- **Product backends** handle domain-specific logic (port 4010-4018)
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- **Web apps** use Next.js 16 + React 19
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- **Mobile apps** use native platforms (SwiftUI, Jetpack Compose, React Native)
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## Style
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Make sure the code follows existing conventions:
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- TypeScript: ESM, strict types, Zod validation
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- Services: Fastify 5 with types.ts → repository.ts → routes.ts pattern
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- Cosmos DB: All documents include productId field
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- No console.log in production (use req.log/app.log in Fastify, structlog in Python)
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- Commit messages: type(scope): description
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- Colors: Use design tokens from @bytelyst/design-tokens, never hardcode
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## Target Branch
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Measure against the main branch of the respective repo since that will be the branch we will merge into.
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## Comments
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Do not make code changes directly. Instead, suggest changes so the reviewer can evaluate them manually.
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## Grammar
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Do not include em dash in any outputs.
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## Summary
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At the end, provide a numbered list of all potential issues found. Each issue should have a number so it can be referred to easily (e.g. "3").
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Also include a summary and explanation of the change of the PR or diff in general from the target branch. Use mermaid diagrams, anecdotes, and any other format you see fit.
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