/prompt-optimizer
Prompt 优化助手。适用于用户想优化提示词、改进 AI 指令、为特定任务设计更好的 prompt,或需要选择合适提示框架时使用。会根据任务场景匹配合适框架,必要时先追问关键信息,再输出更清晰、更可执行的提示词版本。
$ npx -y skills add chujianyun/skills --skill prompt-optimizer --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/prompt-optimizer
Context preview
The summary Claude sees to decide when to auto-load this skill.
Prompt 优化助手。适用于用户想优化提示词、改进 AI 指令、为特定任务设计更好的 prompt,或需要选择合适提示框架时使用。会根据任务场景匹配合适框架,必要时先追问关键信息,再输出更清晰、更可执行的提示词版本。
SKILL.md
prompt-optimizer.SKILL.mdname: prompt-optimizer
description: Prompt 优化助手。适用于用户想优化提示词、改进 AI 指令、为特定任务设计更好的 prompt,或需要选择合适提示框架时使用。会根据任务场景匹配合适框架,必要时先追问关键信息,再输出更清晰、更可执行的提示词版本。
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](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:**
| Complexity | Recommended 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:**
| Domain | Recommended Frameworks | |--------|----------------------| | Marketing Content | BAB, SPEAR, Challenge-Solution-Benefit, BLOG, PROMPT, RHODES | | Decision Analysis | RICE, Pros and Cons, Six Thinking Hats, Tree of Thought, PAUSE, What If | | Education & Training | Bloom's Taxonomy, ELI5, Socratic Method, PEE, Hamburger Model | | Product Development | SCAMPER, HMW, CIDI, RELIC, 3Cs Model | | AI Dialogue/Assistant | COAST, ROSES, TRACE, RACE, RASCEF | | Writing & Creation | BLOG, 4S Method, Hamburger Model, Few-shot, RHODES, Chain of Destiny | | Image Generation | Atomic Prompting | | Quick Simple Tasks | Zero-shot, ERA, TAG, APE, RTF | | Complex Reasoning | Chain 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 Says | Recommended 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 |
Read more
name: prompt-optimizer description: Prompt 优化助手。适用于用户想优化提示词、改进 AI 指令、为特定任务设计更好的 prompt,或需要选择合适提示框架时使用。会根据任务场景匹配合适框架,必要时先追问关键信息,再输出更清晰、更可执行的提示词版本。
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](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:**
| Complexity | Recommended 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:**
| Domain | Recommended Frameworks | |--------|----------------------| | Marketing Content | BAB, SPEAR, Challenge-Solution-Benefit, BLOG, PROMPT, RHODES | | Decision Analysis | RICE, Pros and Cons, Six Thinking Hats, Tree of Thought, PAUSE, What If | | Education & Training | Bloom's Taxonomy, ELI5, Socratic Method, PEE, Hamburger Model | | Product Development | SCAMPER, HMW, CIDI, RELIC, 3Cs Model | | AI Dialogue/Assistant | COAST, ROSES, TRACE, RACE, RASCEF | | Writing & Creation | BLOG, 4S Method, Hamburger Model, Few-shot, RHODES, Chain of Destiny | | Image Generation | Atomic Prompting | | Quick Simple Tasks | Zero-shot, ERA, TAG, APE, RTF | | Complex Reasoning | Chain 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 Says | Recommended 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 |
这是悟鸣公开维护的一组 Agent Skills,偏实战、偏工作流,主要服务于 AI 工具使用、文档解读、项目配置、运维排障与技能分发。 和“技能清单”相比,这个仓库更适合被理解成一张能力地图:每个 skill 不只是一个说明文件,而是围绕某一类任务组织起来的可复用能力单元。
Repo: chujianyun/skills
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