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d2f3886ae1
Repository Restructure: - Move all 83 agent .md files to agents/ subdirectory - Add 15 workflow orchestrators from commands repo to workflows/ - Add 42 development tools from commands repo to tools/ - Update README for unified repository structure The commands repository functionality is now fully integrated, providing complete workflow orchestration and development tooling alongside agents. Directory Structure: - agents/ - 83 specialized AI agents - workflows/ - 15 multi-agent orchestration commands - tools/ - 42 focused development utilities No breaking changes to agent functionality - all agents remain accessible with same names and behavior. Adds workflow and tool commands for enhanced multi-agent coordination capabilities.
68 lines
1.6 KiB
Markdown
68 lines
1.6 KiB
Markdown
---
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model: claude-sonnet-4-0
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---
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# AI/ML Code Review
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Perform a specialized AI/ML code review for: $ARGUMENTS
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Conduct comprehensive review focusing on:
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1. **Model Code Quality**:
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- Reproducibility checks
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- Random seed management
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- Data leakage detection
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- Train/test split validation
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- Feature engineering clarity
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2. **AI Best Practices**:
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- Prompt injection prevention
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- Token limit handling
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- Cost optimization
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- Fallback strategies
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- Timeout management
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3. **Data Handling**:
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- Privacy compliance (PII handling)
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- Data versioning
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- Preprocessing consistency
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- Batch processing efficiency
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- Memory optimization
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4. **Model Management**:
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- Version control for models
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- A/B testing setup
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- Rollback capabilities
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- Performance benchmarks
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- Drift detection
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5. **LLM-Specific Checks**:
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- Context window management
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- Prompt template security
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- Response validation
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- Streaming implementation
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- Rate limit handling
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6. **Vector Database Review**:
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- Embedding consistency
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- Index optimization
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- Query performance
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- Metadata management
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- Backup strategies
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7. **Production Readiness**:
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- GPU/CPU optimization
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- Batching strategies
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- Caching implementation
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- Monitoring hooks
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- Error recovery
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8. **Testing Coverage**:
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- Unit tests for preprocessing
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- Integration tests for pipelines
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- Model performance tests
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- Edge case handling
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- Mocked LLM responses
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Provide specific recommendations with severity levels (Critical/High/Medium/Low). Include code examples for improvements and links to relevant best practices.
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