Deep-agents-With-Langchain

AWS cloud expertise. Use when the user asks about AWS services (EC2, S3, Lambda, IAM, DynamoDB, RDS, ECS, EKS, CloudWatch, Bedrock, SageMaker), architecture design on AWS, boto3 / AWS CLI usage, cost optimization, or cloud security best practices. Provides decision frameworks, CLI/boto3 workflows, and worked examples.

LangGraph expertise for building stateful, multi-step agent workflows. Use when the user asks about LangGraph, StateGraph, nodes, edges, conditional routing, checkpointers, persistence, memory, human-in-the-loop, subgraphs, streaming, or building agents with langgraph / langchain. Provides architecture patterns, API workflows, and runnable examples.

Expert Python programming skill. Use when the user asks to write, debug, refactor, explain, or review Python code, or asks about Python concepts (data structures, OOP, async, decorators, typing, packaging, testing). Provides coding standards, step-by-step workflows, and worked examples.

Report writing skill that should be applied AFTER answering any user query. Whenever the deep agent produces a final answer, use this skill to also write a structured markdown report of the interaction (question, approach, findings, answer, sources) and save it as a file using the write_file tool. Use for every substantive answer, and especially when the user asks for a report, summary document, or saved output.