langgraph-implementation
Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.
llm-artifacts-detection
Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.
llm-judge
LLM-as-judge methodology for comparing code implementations across repositories. Scores implementations on functionality, security, test quality, overengineering, and dead code using weighted rubrics. Used by /beagle:llm-judge command.
postgres-code-review
Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.
prometheus-go-code-review
Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.
pydantic-ai-agent-creation
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
pydantic-ai-common-pitfalls
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
pydantic-ai-dependency-injection
Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.
pydantic-ai-model-integration
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
pydantic-ai-testing
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
pytest-code-review
Reviews pytest test code for async patterns, fixtures, parametrize, and mocking. Use when reviewing test_*.py files, checking async test functions, fixture usage, or mock patterns.
python-code-review
Reviews Python code for type safety, async patterns, error handling, and common mistakes. Use when reviewing .py files, checking type hints, async/await usage, or exception handling.
react-flow-advanced
Advanced React Flow patterns for complex use cases. Use when implementing sub-flows, custom connection lines, programmatic layouts, drag-and-drop, undo/redo, or complex state synchronization.
react-flow-architecture
Architectural guidance for building node-based UIs with React Flow. Use when designing flow-based applications, making decisions about state management, integration patterns, or evaluating whether React Flow fits a use case.
react-flow-code-review
Reviews React Flow code for anti-patterns, performance issues, and best practices. Use when reviewing code that uses @xyflow/react, checking for common mistakes, or optimizing node-based UI implementations.
react-flow-implementation
Implements React Flow node-based UIs correctly using @xyflow/react. Use when building flow charts, diagrams, visual editors, or node-based applications with React. Covers nodes, edges, handles, custom components, state management, and viewport control.
react-flow
React Flow (@xyflow/react) for workflow visualization with custom nodes and edges. Use when building graph visualizations, creating custom workflow nodes, implementing edge labels, or controlling viewport. Triggers on ReactFlow, @xyflow/react, Handle, NodeProps, EdgeProps, useReactFlow, fitView.
react-router-code-review
Reviews React Router code for proper data loading, mutations, error handling, and navigation patterns. Use when reviewing React Router v6.4+ code, loaders, actions, or navigation logic.
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