prompting

# 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 instruction