AgentSkillsCN

prompting

基于Anthropic最佳实践的AI代理提示工程标准和上下文工程原则。涵盖清晰度、结构、渐进发现和信噪比优化。

SKILL.md
--- frontmatter
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")

Supplementary Resources

For full standards: @plugins/meta-work/docs/HOW_TO_PROMPT_ENGINEERING.md

Based On

Anthropic's "Effective Context Engineering for AI Agents"