prompting

To craft prompts for Claude Sonnet 4.5, clearly state goals, constraints, and evaluation criteria to prevent context-collapse. This skill guides you to include concrete examples and rationale, ensuring your instructions are explicit and repeatable across LLM interactions.

LLM prompts fail in multi-step reasoning when they lack explicit rationale and constraints. This skill addresses this by emphasizing why-first reasoning, helping you design prompts that clearly state motivations and evaluation criteria to maximize production workflow reliability.

The best way to structure agent instructions is to provide clear constraints, evaluation criteria, and rationale. This skill enables proactive prompt design, ensuring your agent instructions distinguish between when to implement changes versus only suggesting them.

Yes, this approach applies to prompt engineering in production workflows involving Claude Sonnet 4.5 and Opus 4.5. It supports designing prompts for multi-step reasoning and tool use, ensuring reliability through explicit constraints and clear action versus suggestion guidelines.

You can prevent context-collapse in LLM templates by ensuring prompts are explicit and include thorough constraints. This skill provides guidelines to avoid brevity biases, guiding you to craft instructions that clearly state goals and evaluation criteria for reliable model interactions.