Lubu Labs@Langchain Agent Skills

Use the write_todos tool effectively for task planning and decomposition in Deep Agents. Use when users want to (1) implement task planning with write_todos, (2) break down complex tasks into subtasks, (3) track agent progress through todos, (4) debug why todos aren't completing, (5) design todo structures for different task types (research, coding, analysis), (6) understand todo status lifecycle and best practices, or (7) visualize todo progression from LangSmith traces.

Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.