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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

593

周安装

24

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公开资料未说明

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186
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

2

许可证

unknown

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/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

简介

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。

SKILL.md

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

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