What makes Prompt Optimizer Cn notable?

1.1K downloads. Trust evidence available

How should Prompt Optimizer Cn be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

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Prompt Optimizer Cn

实用提示词优化工具 - 检测原提示词缺失要素（角色/步骤/格式/约束），智能补全后输出清晰易懂的优化版，帮助用户的意图更容易被 AI 理解执行。Use when user says 优化提示词 / 改进prompt / 优化一下 / optimize prompt. Skill: Prompt Optimizer Cn Owner: ucsdzehualiu Summary: 实用提示词优化工具 - 检测原提示词缺失要素（角色/步骤/格式/约束），智能补全后输出清晰易懂的优化版，帮助用户的意图更容易被 AI 理解执行。Use when user says 优化提示词 / 改进prompt / 优化一下 / optimize prompt. Tags: latest:2.1.0 Version history: v2.1.0 | 2026-06-22T07:28:00.072Z | user v2.1.0: Fix metadata (ASCII slug prompt-optimizer-cn, trigger routing via description), add version/author, consolidate triggers, add When NOT to use sec

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

2.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 2.1.0release · observed Jun 22, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17bk47xgrxy45rkmxpwv5p1z983nsd8:prompt-optimizer-cn

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-ucsdzehualiu-prompt-optimizer-cn/snapshot"

Documentation

CLAWHUB

24,878 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: prompt-optimizer-cn
description: 实用提示词优化工具 - 检测原提示词缺失要素（角色/步骤/格式/约束），智能补全后输出清晰易懂的优化版，帮助用户的意图更容易被 AI 理解执行。Use when user says 优化提示词 / 改进prompt / 优化一下 / optimize prompt.
version: 2.1.0
author: prompt-optimizer-cn
tags:
  - prompt-optimization
  - prompt-engineering
---

> **Language**: 自动检测输入语言并用相同语言回复（中文输入→中文回复，English input→English reply）

## 这个工具做什么

帮你把**模糊、简陋的 prompt** 改成**清晰、AI 容易理解执行的版本**。

**核心流程**（5 步）：
1. **诊断**：分析原 prompt 缺什么（角色？步骤？输出格式？约束？）
2. **补全**：按需补上缺失要素（不需要的不强加）
3. **输出**：优化后的 prompt（代码块包裹，方便复制）+ 一行说明改了什么
4. **收集反馈**：询问用户是否满意
5. **迭代**：根据反馈调整直到满意

## 实用框架（RTCF）

补全时参考这 4 个维度（不是每个都必须有，按需补）：

- **R (Role)**: 角色 —— "你是XX专家"，让 AI 进入专业状态
- **T (Task)**: 任务 —— 明确要做什么，避免歧义
- **C (Context)**: 上下文 —— 背景、约束、边界条件
- **F (Format)**: 格式 —— 输出结构（表格/分步骤/代码块/JSON）

## 真实示例（前后对比）

### 示例 1：模糊任务 → 明确角色+格式

**优化前**：
```
帮我写一篇文章
```

**优化后**：
```
你是一位资深科技博客作者，擅长用通俗语言解释技术概念。

请撰写一篇 800 字的技术博客文章，主题：[请用户补充主题]

文章结构：
1. 引人入胜的开头（100字）
2. 核心概念解释（400字，用类比/示例）
3. 实际应用场景（200字）
4. 总结（100字）

目标读者：对技术感兴趣但非专业开发者
```

**改了什么**：补充角色定义、明确字数、拆解结构、指定受众

---

### 示例 2：单句指令 → 分步骤+约束

**优化前**：
```
分析这份数据
```

**优化后**：
```
请对提供的数据进行分析，按以下步骤进行：

1. **数据概览**：数据量、字段类型、缺失值情况
2. **关键指标**：计算均值、中位数、异常值
3. **趋势发现**：找出 3 个最显著的趋势或异常
4. **结论**：用 2-3 句话总结核心发现

输出格式：Markdown 表格 + 文字说明
约束：如果数据有明显错误（如负数年龄），先指出再分析
```

**改了什么**：拆分步骤、增加输出格式、加约束条件

---

### 示例 3：技术任务 → 加角色+示例

**优化前**：
```
写个 Python 函数计算斐波那契数列
```

**优化后**：
```
你是一位 Python 专家，注重代码可读性和性能。

请编写一个 Python 函数 `fibonacci(n)`，计算斐波那契数列的第 n 项。

要求：
- 使用迭代而非递归（性能考虑）
- 加 docstring 说明参数和返回值
- 处理边界情况（n <= 0）
- 加 3 个测试用例

示例输出格式（使用缩进代替内部代码块）：

    def fibonacci(n: int) -> int:
        """计算斐波那契数列第 n 项
        
        Args:
            n: 正整数，目标项数
        Returns:
            第 n 项的值
        """
        # 实现...

```

**改了什么**：补角色、明确实现方式、加边界处理要求、给输出示例

---

### 示例 4：创意任务 → 加风格+受众

**优化前**：
```
给我的产品写个宣传语
```

**优化后**：
```
你是资深广告文案，擅长洞察用户痛点并用简短有力的语言打动人心。

请为产品"[产品名]"创作 3 条宣传语，每条不超过 15 字。

产品特点：[请用户补充，如"AI驱动的日程管理工具"]
目标用户：[请用户补充，如"忙碌的职场人士"]

风格要求：
- 直击痛点，避免空洞形容词
- 口语化，易记
- 突出核心价值而非功能堆砌

输出格式：
1. [宣传语] —— [一句话说明为什么这样写]
2. [宣传语] —— [理由]
3. [宣传语] —— [理由]
```

**改了什么**：补角色、要求明确产品信息、定义风格、要求给出理由

---

### 示例 5：英文示例

**Before**:
```
Explain quantum computing
```

**After**:
```
You are a physics educator skilled at explaining complex concepts to non-experts using everyday analogies.

Please explain quantum computing in simple terms.

Structure:
1. What it is (50 words, use an analogy)
2. How it differs from classical computing (100 words)
3. One real-world application (50 words)

Audience: College students with no physics background
Constraints: Avoid jargon like "superposition" unless you explain it first
```

**What changed**: Added role, structure, audience, jargon constraint

---

## 工作流程

### Step 1: 诊断原 prompt（内部判断，不告诉用户）

- 缺角色定义？（"你是XX专家"）
- 任务模糊？（"帮我做XX" → 做什么具体的？）
- 缺输出格式？（表格？分步？代码块？）
- 有歧义？（一句话有多种理解）
- 缺约束/边界？（什么不该做？边界情况怎么处理？）

### Step 2: 按需补全

**原则**：
- ✅ 按需补充（不是每个都要有）
- ✅ 保留原始意图
- ✅ 不改变用户想要的核心内容
- ❌ 不过度工程化（简单任务不要搞复杂）

### St

_meta.json

{
  "ownerId": "kn7ecgbzbya89gesghbv1kty9d82r8e4",
  "slug": "prompt-optimizer-cn",
  "version": "2.1.0",
  "publishedAt": 1782113280072
}

skill-card.md

## Description:

Prompt Optimizer Cn helps agents rewrite vague prompts into clearer, ready-to-use prompts by diagnosing missing role, task, context, format, and constraints.

This skill is ready for commercial/non-commercial use.

## Publisher:

[ucsdzehualiu](https://clawhub.ai/user/ucsdzehualiu)

### License/Terms of Use:

MIT

## Use Case:

Agent users and prompt engineers use this skill to turn unclear Chinese or English prompts into structured, executable prompts while preserving the user's original intent.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Broad trigger wording could activate prompt optimization when the user intended a different task.

Mitigation: Use the skill only for explicit prompt optimization requests and preserve the user's original intent when rewriting.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/ucsdzehualiu/skills/prompt-optimizer-cn)
- [ClawHub Publisher Profile](https://clawhub.ai/user/ucsdzehualiu)

## Skill Output:

**Output Type(s):** [text, markdown, guidance]

**Output Format:** [Markdown with a fenced optimized prompt, a one-line change summary, and a follow-up question.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Matches the user's input language and preserves the original prompt intent.]

## Skill Version(s):

2.1.0 (source: frontmatter and server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

LICENSE

MIT License

Copyright (c) 2026 prompt-optimizer-cn

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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