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prompting

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.

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Files included (2)

  • SKILL.md2.6 KB
  • CLAUDE.md8.6 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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"

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