Token导航 LogoToken导航

Agent Skill · 研究检索

prompt-optimizer(提示优化器)

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。

prompt-optimizer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 51,124。

总安装 593周安装 24GitHub Stars 公开资料未说明下载 186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
unknown

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/aleister1102/skills
仓库路径:skills/prompt-optimizer
安装命令:
npx skills add https://github.com/aleister1102/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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aleister1102/skills --skill prompt-optimizer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

prompt-optimizer 将模糊提示转化为结构化、可执行的规格说明。采用 EARS 方法论提升提示词的清晰度与可操作性。适用于系统指令编写、Agent 行为约束定义等工作流优化。需结合具体业务上下文调整,避免将示例直接固化为规则。

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。

来源仓库内路径:skills/prompt-optimizer

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/aleister1102/skills --skill prompt-optimizer;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

prompt-optimizer 怎么安装?

在终端执行 npx skills add https://github.com/aleister1102/skills --skill prompt-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prompt-optimizer 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。

prompt-optimizer 需要什么权限?

权限级别是只读。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。

prompt-optimizer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

prompt-optimizer 用的人多吗?

公开数据是累计安装 593 次,近一周安装 24 次,下载 186 次。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。

prompt-optimizer 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
prompt-optimizer

提示优化器 · 本页

593—只读未明确有提醒

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

Prompt Optimizer

Overview

Optimize vague prompts into precise, actionable specifications using EARS (Easy Approach to Requirements Syntax) - a Rolls-Royce methodology for transforming natural language into structured, testable requirements.

Methodology inspired by: This skill's approach to combining EARS with domain theory grounding was inspired by 阿星AI工作室 (A-Xing AI Studio), which demonstrated practical EARS application for prompt enhancement.

Four-layer enhancement process:

  1. EARS syntax transformation - Convert descriptive language to normative specifications
  2. Domain theory grounding - Apply relevant industry frameworks (GTD, BJ Fogg, Gestalt, etc.)
  3. Example extraction - Surface concrete use cases with real data
  4. Structured prompt generation - Format using Role/Skills/Workflows/Examples/Formats framework

When to Use

Apply when:

  • User provides vague feature requests ("build a dashboard", "create a reminder app")
  • Requirements lack specific conditions, triggers, or measurable outcomes
  • Natural language descriptions need conversion to testable specifications
  • User explicitly requests prompt optimization or requirement refinement

Six-Step Optimization Workflow

Step 1: Analyze Original Requirement

Identify weaknesses:

  • Overly broad - "Add user authentication" → Missing password requirements, session management
  • Missing triggers - "Send notifications" → Missing when/why notifications trigger
  • Ambiguous actions - "Make it user-friendly" → No measurable usability criteria
  • No constraints - "Process payments" → Missing security, compliance requirements

Step 2: Apply EARS Transformation

Convert requirements to EARS patterns. See references/ears_syntax.md for complete syntax rules.

Five core patterns:

  1. Ubiquitous: The system shall <action>
  2. Event-driven: When <trigger>, the system shall <action>
  3. State-driven: While <state>, the system shall <action>
  4. Conditional: If <condition>, the system shall <action>
  5. Unwanted behavior: If <condition>, the system shall prevent <unwanted action>

Quick example:

Before: "Create a reminder app with task management"

After (EARS):
1. When user creates a task, the system shall guide decomposition into executable sub-tasks
2. When task deadline is within 30 minutes AND user has not started, the system shall send notification with sound alert
3. When user completes a sub-task, the system shall update progress and provide positive feedback

Transformation checklist:

  • Identify implicit conditions and make explicit
  • Specify triggering events or states
  • Use precise action verbs (shall, must, should)
  • Add measurable criteria ("within 30 minutes", "at least 8 characters")
  • Break compound requirements into atomic statements
  • Remove ambiguous language ("user-friendly", "fast")

Step 3: Identify Domain Theories

Match requirements to established frameworks. See references/domain_theories.md for full catalog.

Common domain mappings:

  • Productivity → GTD, Pomodoro, Eisenhower Matrix
  • Behavior Change → BJ Fogg Model (B=MAT), Atomic Habits
  • UX Design → Hick's Law, Fitts's Law, Gestalt Principles
  • Security → Zero Trust, Defense in Depth, Privacy by Design

Selection process:

  1. Identify primary domain from requirement keywords
  2. Match to 2-4 complementary theories
  3. Apply theory principles to specific features
  4. Cite theories in enhanced prompt for credibility

Step 4: Extract Concrete Examples

Generate specific examples with real data:

  • User scenarios: "When user logs in on mobile device..."
  • Data examples: "Product: 'Laptop', Price: $999, Stock: 15"
  • Workflow examples: "Task: Write report → Sub-tasks: Research (2h), Draft (3h), Edit (1h)"

Examples must be realistic, specific, varied (success/error/edge cases), and testable.

Step 5: Generate Enhanced Prompt

Structure using the standard framework:

# Role

[Specific expert role with domain expertise]

## Skills

- [Core capability 1]
- [Core capability 2]
  [List 5-8 skills aligned with domain theories]

## Workflows

1. [Phase 1] - [Key activities]
2. [Phase 2] - [Key activities]
   [Complete step-by-step process]

## Examples

[Concrete examples with real data, not placeholders]

## Formats

[Precise output specifications:

- File types, structure requirements
- Design/styling expectations
- Technical constraints
- Deliverable checklist]

Quality criteria:

  • Role specificity: "Product designer specializing in time management apps" > "Designer"
  • Theory grounding: Reference frameworks explicitly
  • Actionable workflows: Clear inputs/outputs and decision points
  • Concrete examples: Real data, not "Example 1", "Example 2"
  • Measurable formats: Specific requirements, not "good design"

Step 6: Present Optimization Results

Output in structured format:

## Original Requirement

[User's vague requirement]

**Identified Issues:**

- [Issue 1: e.g., "Lacks specific trigger conditions"]
- [Issue 2: e.g., "No measurable success criteria"]

## EARS Transformation

[Numbered list of EARS-formatted requirements]

## Domain & Theories

**Primary Domain:** [e.g., Authentication Security]

**Applicable Theories:**

- **[Theory 1]** - [Brief relevance]
- **[Theory 2]** - [Brief relevance]

## Enhanced Prompt

[Complete Role/Skills/Workflows/Examples/Formats prompt]

---

**How to use:**
[Brief guidance on applying the prompt]

Advanced Techniques

For complex scenarios, see references/advanced_techniques.md:

  • Multi-stakeholder requirements - EARS statements for each user type
  • Non-functional requirements - Performance, security, scalability with quantified thresholds
  • Complex conditional logic - Nested conditions with boolean operators

Quick Reference

Do's: ✅ Break down compound requirements (one EARS statement per requirement) ✅ Specify measurable criteria (numbers, timeframes, percentages) ✅ Include error/edge cases ✅ Ground in established theories ✅ Use concrete examples with real data

Don'ts: ❌ Avoid vague language ("fast", "user-friendly") ❌ Don't assume implicit knowledge ❌ Don't mix multiple actions in one statement ❌ Don't use placeholders in examples

Resources

Load these reference files as needed:

  • references/ears_syntax.md - Complete EARS syntax rules, all 5 patterns, transformation guidelines, benefits
  • references/domain_theories.md - 40+ theories mapped to 10 domains (productivity, UX, gamification, learning, e-commerce, security, etc.)
  • references/examples.md - Four complete transformation examples (procrastination app, e-commerce product page, learning dashboard, password reset security) with before/after comparisons and reusable template
  • references/advanced_techniques.md - Multi-stakeholder requirements, non-functional specs, complex conditional logic patterns

When to load references:

  • EARS syntax clarification needed → ears_syntax.md
  • Domain theory selection requires extensive options → domain_theories.md
  • User requests multiple optimization examples → examples.md
  • Complex requirements with multiple stakeholders or non-functional specs → advanced_techniques.md

平台分布

Codex

33.48%
按下载量换算62

Claude

27.95%
按下载量换算52

Cursor

20.27%
按下载量换算38

Gemini CLI

9.54%
按下载量换算18

安全审计

Gen Agent Trust Hub

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills