| name | prompting |
| description | Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio. |
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
