What problem does it solve? Teams building an AI Agent Platform need generated code that consistently follows their architecture: Python 3.14, FastAPI, LangGraph orchestration, CrewAI sub-agents, Pydantic v2, and SQLAlchemy 2.0. Without enforced standards, generated code drifts into hardcoded LLM providers, missing type hints, inconsistent logging, and regex-based routing decisions. ## Core Features & Use Cases - Standards-enforced code generation: Produces Python code with mandatory type hints, built-in generics, explicit return types, and structured timestamped logging via a centralized logging config. - Framework-specific patterns: Provides ready templates for LangGraph async nodes, CrewAI crew definitions, LangChain @tool wrappers, Pydantic schemas, and SQLAlchemy declarative models. - Provider-agnostic LLM usage: Enforces the shared get_llm() factory instead of hardcoded ChatOpenAI, with per-component provider overrides. - Use Case: Ask for a new LangGraph workflow node or a CrewAI research agent, and receive code that already includes logging, typing, registry integration, and correct imports. ## Quick Start Ask the assistant to generate a new FastAPI endpoint or LangGraph node for the AI Agent Platform following the coding-skill standards.
