customaize▌

Context is the complete state available to a language model at inference time. It includes everything the model can attend to when generating responses: system instructions, tool definitions, retrieved documents, message history, and tool outputs. Understanding context fundamentals is prerequisite to effective context engineering.

Advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

This meta-command helps create other commands by:

Analyze the project, suggest practical hooks, and create them with proper testing.

Test skill provided by user or developed before.

Evaluation of agent systems requires different approaches than traditional software or even standard language model applications. Agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Effective evaluation must account for these characteristics while providing actionable feedback. A robust evaluation framework enables continuous improvement, catches regressions, and validates that context engineering choices achieve intended effects.

Apply Anthropic's official skill authoring best practices to your skill.

This command provides guidance for creating effective skills.

Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit.

Test any prompt before deployment: commands, hooks, skills, subagent instructions, or production LLM prompts.