promptinglisted

# Prompting Skill ## When to Activate This Skill - Prompt engineering questions - Context engineering guidance - AI agent design - Prompt structure help - Best practices for LLM prompts - Agent configuration ## Core Philosophy **Context engineering** = Curating optimal set of tokens during LLM inference **Primary Goal:** Find smallest possible set of high-signal tokens that maximize desired outcomes ## Key Principles ### 1. Context is Finite Resource - LLMs have limited "attention budget" - Performance degrades as context grows - Every token depletes capacity - Treat context as precious ### 2. Optimize Signal-to-Noise - Clear, direct language over verbose explanations - Remove redundant information - Focus on high-value tokens ### 3. Progressive Discovery - Use lightweight identifiers vs full data dumps - Load detailed info dynamically when needed - Just-in-time information loading ## Markdown Structure Standards Use clear semantic sections: - **Background Information**: Minimal essential context - **Instructions**: Imperative voice, specific, actionable - **Examples**: Show don't tell, concise, representative - **Constraints**: Boundaries, limitations, success criteria ## Writing Style ### Clarity Over Completeness ✅ Good: "Validate input before processing" ❌ Bad: "You should always make sure to validate..." ### Be Direct ✅ Good: "Use calculate_tax tool with amount and jurisdiction" ❌ Bad: "You might want to consider using..." ### Use Structured Lists ✅ Good: Bu