Prompting
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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.
SKILL.md
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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: Bulleted constraints ❌ Bad: Paragraph of requirements
Context Management
Just-in-Time Loading
Don't load full data dumps - use references and load when needed
Structured Note-Taking
Persist important info outside context window
Sub-Agent Architecture
Delegate subtasks to specialized agents with minimal context
Best Practices Checklist
- Uses Markdown headers for organization
- Clear, direct, minimal language
- No redundant information
- Actionable instructions
- Concrete examples
- Clear constraints
- Just-in-time loading when appropriate
Anti-Patterns
❌ Verbose explanations ❌ Historical context dumping ❌ Overlapping tool definitions ❌ Premature information loading ❌ Vague instructions ("might", "could", "should")
Supplementary Resources
For full standards: read ${PAI_DIR}/skills/prompting/CLAUDE.md
Based On
Anthropic's "Effective Context Engineering for AI Agents"