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Agent Skill · AI 工具

system-prompt-engineering(系统提示工程)

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。

system-prompt-engineering 是AI 工具类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证为 MIT,暂无第三方扫描结果,按安装量在AI 工具类的 2,536 个 Skill 里排第 550。

总安装 3,305周安装 135GitHub Stars 61下载 1,058核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

AI 工具类 Skill 用来扩展助手本身的能力,例如接模型、接检索、接外部服务。

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。

来源仓库内路径:skills/system-prompt-engineering

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/melodic-software/claude-code-plugins --skill s…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

system-prompt-engineering 怎么安装?

在终端执行 npx skills add https://github.com/melodic-software/claude-code-plugins --skill system-prom…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

system-prompt-engineering 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

system-prompt-engineering 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

system-prompt-engineering 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

system-prompt-engineering 用的人多吗?

公开数据是累计安装 3,305 次,近一周安装 135 次,下载 1,058 次,GitHub 61 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

system-prompt-engineering 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理AI 工具类任务、又不想每次都手写提示词的人。

不太适合

  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

AI 工具类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
executing-plans

执行计划 · Codex、Claude

1,515,336173,110只读MIT通过
verification-before-completion

完成前的验证 · Codex、Claude

1,319,472173,121权限未标注MIT通过
subagent-driven-development

子 Agent 驱动开发 · Codex、Claude

1,312,416173,138只读MIT通过
copy-editing

复制编辑 · Codex、Claude

1,209,57625,632只读MIT通过
paywall-upgrade-cro

付费专区升级 cro · Codex、Claude

1,030,17625,617只读MIT通过
system-prompt-engineering

系统提示工程 · 本页

3,30561权限未标注MIT未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

Cursor

27.51%
按下载量换算291

Antigravity

22.45%
按下载量换算238

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

安全审计

暂无安全审计结果可展示。

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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