What problem does it solve? Choosing between adopting an agent framework (LangGraph, CrewAI, AutoGen, Claude Agent SDK, etc.) and building your own harness is a high-stakes architectural decision with churn, licensing, and lock-in risks. This Skill provides a pre-researched decision framework so you do not repeat that analysis from scratch. ## Core Features & Use Cases - Dependency Test: A single rule for any dependency — if it sees prompts and tokens, own it; if not, adopt it as ordinary infrastructure. - Build/Adopt Matrix by Layer: Concrete calls for HTTP transport, message abstraction, agent loop, harness features, sandboxing, durable execution, and tracing. - Churn Evidence & Anti-Recommendations: Measured evidence of framework churn (AutoGen and Semantic Kernel in maintenance mode, pre-1.0 SDKs) plus flat anti-recommendations. - Use Case: When a proposal suggests adding LangGraph or the OpenAI Agents SDK to your agent loop, apply this Skill to evaluate whether the dependency belongs in the model-facing path or the execution path. ## Quick Start Ask the agent to evaluate whether we should adopt LangGraph for our agent orchestration using the harness-selection criteria.