Agent优化
- 作者仓库星标 1,187
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- 作者更新于 2026年6月14日 10:01
- 作者仓库 claude-code-skills
- 领域
- AI 智能
- 兼容 Agent
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- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- 无特殊要求
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- 只读
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- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: prompt-optimizer
description: Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach t…
category: AI 智能
runtime: 无特殊运行时
---
# prompt-optimizer 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Overview / When to Use / Six-Step Optimization Workflow”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Overview / When to Use / Six-Step Optimization Workflow”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Overview / When to Use / Six-Step Optimization Workflow”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: prompt-optimizer
description: Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach t…
category: AI 智能
source: daymade/claude-code-skills
---
# prompt-optimizer
## 什么时候使用
- prompt-optimizer 是 AI 智能方向的技能,主要扩展 Agent 在调模型、改提示词、跑评测这类场景下的能力 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Overview / When to Use / Six-Step Optimization Workflow」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "prompt-optimizer" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Overview / When to Use / Six-Step Optimization Workflow
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} 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:
- EARS syntax transformation - Convert descriptive language to normative specifications
- Domain theory grounding - Apply relevant industry frameworks (GTD, BJ Fogg, Gestalt, etc.)
- Example extraction - Surface concrete use cases with real data
- 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:
- Ubiquitous:
The system shall <action> - Event-driven:
When <trigger>, the system shall <action> - State-driven:
While <state>, the system shall <action> - Conditional:
If <condition>, the system shall <action> - 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:
- Identify primary domain from requirement keywords
- Match to 2-4 complementary theories
- Apply theory principles to specific features
- 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, benefitsreferences/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 templatereferences/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
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核