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system-prompt-engineering系统提示工程

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

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3,305

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:system-prompt-engineering(系统提示工程)
来源仓库:https://github.com/melodic-software/claude-code-plugins
仓库路径:skills/system-prompt-engineering
安装命令:
npx skills add https://github.com/melodic-software/claude-code-plugins --skill system-prompt-engineering
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/melodic-software/claude-code-plugins --skill system-prompt-engineering

简介

用于辅助提示词、系统指令和工作流模板的规范化管理。

  • 适合统一 Agent 行为边界、输出格式或操作步骤。
  • 可帮助拆分复杂任务、增强提示词复用性或定义失败回退机制。
  • 使用时应保留真实业务约束,勿将示例当作硬性规则。system-prompt-engineering 属于AI 工具类 Skill,可作为该场景下的辅助能力补充。
  • 涉及自动执行或外部工具调用时需明确权限与风险处理策略。

SKILL.md

System Prompt Engineering Skill

Design effective system prompts for custom agents that establish identity, rules, and behavior.

Purpose

Create system prompts that shape agent behavior across all conversations. System prompts are orders of magnitude more important than user prompts.

When to Use

  • Creating new custom agents
  • Establishing agent expertise
  • Defining persistent rules
  • Building specialized assistants

System Prompt vs User Prompt

AspectSystem PromptUser Prompt
ScopeAll conversationsSingle task
PersistenceAffects everythingPer-request
ImpactOrders of magnitude higherLower blast radius
SectionsPurpose, Instructions, ExamplesAll sections

Key Sections

Purpose (Required)

Define who the agent is and what it does.

## Purpose

You are a security expert specializing in code review.
Your role is to identify vulnerabilities and suggest fixes.
You focus on OWASP Top 10 and secure coding practices.

Guidelines:

  • Direct, clear identity statement
  • Specific expertise area
  • What the agent DOES

Instructions (Recommended)

Establish rules, constraints, and boundaries.

## Instructions

- Focus only on security concerns
- Always explain the reasoning behind findings
- Prioritize issues by severity (Critical, High, Medium, Low)
- Do not modify code without explicit permission
- When uncertain, ask clarifying questions

Guidelines:

  • Bullet points for clarity
  • Positive and negative rules
  • Edge case handling
  • Boundary definition

Examples (Critical)

Shape behavior through concrete examples.

## Examples

### Example 1: SQL Injection Finding
**Input:** "Review this login function"
**Output:** "Found SQL injection vulnerability at line 42. The user input is directly concatenated into the query string. Recommendation: Use parameterized queries instead."

### Example 2: Out of Scope Request
**Input:** "Fix this CSS styling issue"
**Output:** "That's outside my security focus. I specialize in security vulnerabilities, not styling. Consider consulting a frontend expert for CSS issues."

### Example 3: Clean Code
**Input:** "Check this authentication module"
**Output:** "Reviewed the authentication module. No security vulnerabilities found. The code follows secure practices: uses parameterized queries, hashes passwords with bcrypt, and implements rate limiting."

Guidelines:

  • 2-4 diverse examples
  • Cover common cases
  • Include edge cases
  • Show desired output format

Design Process

Step 1: Define Agent Identity

Answer:

  • What expertise does this agent have?
  • What domain does it operate in?
  • What is its primary purpose?
  • What tone should it use?

Step 2: Establish Boundaries

Answer:

  • What should the agent do?
  • What should the agent NOT do?
  • When should it ask for clarification?
  • What are its limitations?

Step 3: Create Examples

For each key scenario:

  • What's a typical input?
  • What's the ideal output?
  • How should edge cases be handled?

Step 4: Validate Design

Check:

  • Purpose is clear and specific
  • Instructions are unambiguous
  • Examples cover key scenarios
  • Boundaries are well-defined
  • Tone is consistent

What to Avoid

AvoidWhyInstead
Detailed workflowsReduces autonomyHigh-level guidelines
Dynamic variablesSystem prompt is staticUse user prompts
Prescriptive formatsOver-constrainsFlexible guidelines
Everything "just in case"Context bloatOnly essentials

System Prompt Architecture

---
name: agent-name
description: When to use this agent (for auto-delegation)
tools: [minimal tool set]
model: sonnet
color: blue
---

# Agent Name

## Purpose
[Identity and role definition]

## Instructions
[Rules and constraints]

## Examples

### Example 1: [Scenario]
**Input:** [typical input]
**Output:** [ideal output]

### Example 2: [Edge Case]
**Input:** [edge case input]
**Output:** [handling output]

### Example 3: [Boundary]
**Input:** [out-of-scope request]
**Output:** [how to decline/redirect]

Output Format

When designing a system prompt:

## System Prompt Design

**Agent Name:** [name]
**Domain:** [expertise area]
**Model:** [sonnet/opus/haiku]

### Purpose
[2-3 sentences defining identity]

### Instructions
- [rule 1]
- [rule 2]
- [rule 3]

### Examples

**Example 1:** [scenario]
- Input: [input]
- Output: [output]

**Example 2:** [scenario]
- Input: [input]
- Output: [output]

### Validation
- [ ] Purpose is specific
- [ ] Instructions are actionable
- [ ] Examples are diverse
- [ ] Boundaries are clear

Common Agent Types

Expert Agent

Focus: Deep domain knowledge

## Purpose
You are an expert in [domain] with deep knowledge of [specifics].

Guard Agent

Focus: Validation and safety

## Instructions
- Validate all inputs against [criteria]
- Block requests that [conditions]
- Log suspicious activity

Translator Agent

Focus: Format conversion

## Examples
### Input Format
[format A]

### Output Format
[format B]

Key Quote

"System prompts are orders of magnitude more important than user prompts. They run once and affect everything."

Cross-References

  • @system-vs-user-prompts.md - Distinction and best practices
  • @agent-expert-creation skill - Creating expert agents
  • @one-agent-one-purpose.md - Specialization principle

Version History

  • v1.0.0 (2025-12-26): Initial release

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Cursor

27.51%
按下载量换算291

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22.45%
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Claude Code

19.19%
按下载量换算203

Gemini CLI

13.44%
按下载量换算142

github-copilot

7.2%
按下载量换算76

windsurf

3.12%
按下载量换算33

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