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")

Based On

Anthropic's "Effective Context Engineering for AI Agents"