Validate implementation quality through custom checklists, scoring against constitution standards, specification coverage, and producing remediation recommendations.
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Design technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.
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Execute development tasks to build features, producing code, tests, and configuration artifacts that satisfy specification requirements and comply with constitution standards.
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HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
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Multi-agent swarm formation and coordinated execution with topology-aware agent deployment, consensus protocols, and anti-drift enforcement.
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Entity and fact extraction for user profiling and personalization
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Few-shot example generation and optimization for improved LLM performance
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Use when starting any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs. Evidence before assertions.
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LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.
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LangChain retriever implementation with various retrieval strategies for RAG applications
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Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.
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Perform cross-artifact consistency and coverage analysis across constitution, specification, plan, and task artifacts to detect gaps, conflicts, and misalignments before implementation.
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Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.
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Hugging Face transformer model fine-tuning and inference for intent classification
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LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
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Conditional edge routing and state-based transitions for LangGraph workflows
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Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies.
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Use when implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle.
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Microsoft AutoGen multi-agent configuration for conversational AI systems
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Apply general knowledge reasoning across diverse domains — trivia, commonsense inference, analogy, and factual question answering.
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LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns
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LangChain ReAct agent implementation with tool binding for reasoning and action loops
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LangGraph checkpoint and persistence configuration for stateful workflow management
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