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prompt-optimizer提示优化器

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。

总安装

2,607

周安装

112

GitHub Stars

534

下载量

914
CodexClaudeCursorGemini CLI

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:prompt-optimizer(提示优化器)
来源仓库:https://github.com/chujianyun/skills
仓库路径:skills/prompt-optimizer
安装命令:
npx skills add https://github.com/chujianyun/skills --skill prompt-optimizer
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/chujianyun/skills --skill prompt-optimizer

简介

prompt-optimizer 帮助用户选择与优化提示词框架,提升指令清晰度与执行效率,适合在 Codex、Claude、Cursor、Gemini CLI 中规范化 Agent 行为。

  • 它先分析任务类型再匹配对应框架(如零样本、思维链),避免过度设计简单 prompt。
  • 输出时会解释选择理由,并支持迭代优化直至满足具体场景需求。
  • 涉及高风险操作时应显式加入确认步骤与失败回退机制,确保可控性。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Prompt Optimizer

帮助用户基于具体任务场景,选择合适的提示词框架,并生成更清晰、更可执行的 prompt。

设计模式

本 skill 主要采用:

  • Reviewer:先判断用户现有 prompt 或任务描述的问题
  • Inversion:信息不足时,先追问目标、受众、约束和格式
  • Generator:基于选定框架生成优化后的 prompt

Gotchas

  • 不要一上来就套框架,先判断任务是否真的需要复杂框架
  • 不要为了显得专业而过度设计简单 prompt
  • 如果用户只想快速润色一句 prompt,不要强行输出一整套长模板
  • 如果目标、受众、输出格式不清楚,先补最小必要问题
  • 说明为什么选这个框架,比堆很多框架名更重要

Workflow

Copy this checklist and track your progress:

  • Step 1: Analyze User Input
  • Step 2: Match Scenario and Select Framework
  • Step 3: Load Framework Details
  • Step 4: Clarify Ambiguities
  • Step 5: Generate Optimized Prompt
  • Step 6: Present and Iterate

When a user requests create or prompt optimization, follow these steps:

Step 1: Analyze User Input

Receive the user's request, which may be:

  • A raw prompt that needs optimization
  • A task description or requirement
  • A vague idea that needs to be turned into a prompt

Step 2: Match Scenario and Select Framework

Read the references/Frameworks_Summary.md file to:

  1. Identify the user's scenario from the application scenarios listed
  2. Match the most suitable framework(s) based on:

- Application scenario alignment - Task complexity (simple/medium/complex) - Domain category (marketing, decision analysis, education, etc.)

Framework Selection Guide by Complexity:

ComplexityRecommended Frameworks
Simple (≤3 elements)APE, ERA, TAG, RTF, BAB, PEE, ELI5
Medium (4-5 elements)RACE, CIDI, SPEAR, SPAR, FOCUS, SMART, GOPA, ORID, CARE, ROSE, PAUSE, TRACE, GRADE, TRACI, RODES
Complex (6+ elements)RACEF, CRISPE, SCAMPER, Six Thinking Hats, ROSES, PROMPT, RISEN, RASCEF, Atomic Prompting

Framework Selection Guide by Domain:

DomainRecommended Frameworks
Marketing ContentBAB, SPEAR, Challenge-Solution-Benefit, BLOG, PROMPT, RHODES
Decision AnalysisRICE, Pros and Cons, Six Thinking Hats, Tree of Thought, PAUSE, What If
Education & TrainingBloom's Taxonomy, ELI5, Socratic Method, PEE, Hamburger Model
Product DevelopmentSCAMPER, HMW, CIDI, RELIC, 3Cs Model
AI Dialogue/AssistantCOAST, ROSES, TRACE, RACE, RASCEF
Writing & CreationBLOG, 4S Method, Hamburger Model, Few-shot, RHODES, Chain of Destiny
Image GenerationAtomic Prompting
Quick Simple TasksZero-shot, ERA, TAG, APE, RTF
Complex ReasoningChain of Thought, Tree of Thought

Step 3: Load Framework Details

Once the best framework is identified, read the corresponding framework file from the references/frameworks/ directory:

  • File naming pattern: XX_FrameworkName_Framework.md
  • Example: For RACEF framework, read references/frameworks/01_RACEF_Framework.md

The framework file contains:

  • Framework overview and components
  • Detailed explanation of each element
  • Pros and cons
  • Best practice examples

Step 4: Clarify Ambiguities

Before generating the final prompt, verify with the user:

  1. Goal Clarity: Is the intended outcome clear?
  2. Target Audience: Who will receive the AI's response?
  3. Context Completeness: Is sufficient background information provided?
  4. Format Requirements: Are there specific output format needs?
  5. Constraints: Are there any limitations or restrictions?

Ask clarifying questions if any information is:

  • Missing
  • Ambiguous
  • Incomplete
  • Contradictory

Example clarifying questions:

  • "What specific outcome are you hoping to achieve?"
  • "Who is the target audience for this content?"
  • "Are there any format or length requirements?"
  • "What context should the AI consider?"

Step 5: Generate Optimized Prompt

Apply the selected framework to create the final prompt:

  1. Structure the prompt according to framework components
  2. Incorporate all clarified information
  3. Ensure clarity and specificity
  4. Include relevant examples if the framework requires
  5. Add any necessary constraints or guidelines

Step 6: Present and Iterate

Present the optimized prompt to the user with:

  1. The selected framework name and why it was chosen
  2. The complete optimized prompt
  3. Explanation of how each framework element was applied
  4. Suggestions for potential variations or improvements

If the user requests changes, iterate on the prompt while maintaining framework structure.

Framework Reference Files

All framework details are stored in the references/frameworks/ directory. Each file contains:

  • Application scenarios
  • Framework components with explanations
  • Advantages and disadvantages
  • Multiple practical examples

Quick Framework Selection

For users unsure which framework to use:

User SaysRecommended Framework
"I need a simple prompt"APE, ERA, TAG
"I want to persuade/sell"BAB, SPEAR, Challenge-Solution-Benefit
"I need to analyze/decide"RICE, Pros and Cons, Chain of Thought
"I want to teach/explain"ELI5, Bloom's Taxonomy, Socratic Method
"I need creative ideas"SCAMPER, HMW, SPARK, Imagine
"I want structured writing"BLOG, 4S Method, Hamburger Model
"I need step-by-step reasoning"Chain of Thought, Tree of Thought
"I'm generating images"Atomic Prompting
"I need a detailed plan"RISEN, RASCEF, CRISPE

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

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

Claude Code

28.45%
按下载量换算260

OpenCode

22.52%
按下载量换算206

Gemini CLI

18.15%
按下载量换算166

Antigravity

12.31%
按下载量换算113

Codex

8.09%
按下载量换算74

Cursor

3.61%
按下载量换算33

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。

来源信息

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