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https://github.com/wshobson/agents.git
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ce7a5938c1
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 This prepares the repository for unified plugin marketplace integration. 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.
1.6 KiB
1.6 KiB
model
| model |
|---|
| claude-sonnet-4-0 |
AI/ML Code Review
Perform a specialized AI/ML code review for: $ARGUMENTS
Conduct comprehensive review focusing on:
-
Model Code Quality:
- Reproducibility checks
- Random seed management
- Data leakage detection
- Train/test split validation
- Feature engineering clarity
-
AI Best Practices:
- Prompt injection prevention
- Token limit handling
- Cost optimization
- Fallback strategies
- Timeout management
-
Data Handling:
- Privacy compliance (PII handling)
- Data versioning
- Preprocessing consistency
- Batch processing efficiency
- Memory optimization
-
Model Management:
- Version control for models
- A/B testing setup
- Rollback capabilities
- Performance benchmarks
- Drift detection
-
LLM-Specific Checks:
- Context window management
- Prompt template security
- Response validation
- Streaming implementation
- Rate limit handling
-
Vector Database Review:
- Embedding consistency
- Index optimization
- Query performance
- Metadata management
- Backup strategies
-
Production Readiness:
- GPU/CPU optimization
- Batching strategies
- Caching implementation
- Monitoring hooks
- Error recovery
-
Testing Coverage:
- Unit tests for preprocessing
- Integration tests for pipelines
- Model performance tests
- Edge case handling
- Mocked LLM responses
Provide specific recommendations with severity levels (Critical/High/Medium/Low). Include code examples for improvements and links to relevant best practices.