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。
Best use case
prompt-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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。
Teams using prompt-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-optimizer Compares
| Feature / Agent | prompt-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 |
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