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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

✅ 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"