专业文字编辑与内容润色 - Openclaw Skills
作者:互联网
2026-03-29
什么是 文字编辑?
Openclaw Skills 的文字编辑技能提供了一个强大的框架,将原始文本转化为打磨精炼、可直接发布的发布内容。它通过应用复杂的语言分析来提高可读性、消除句法错误并保持一致的品牌语调,从而弥合了草稿生成与最终审核之间的差距。该技能对于使用 Openclaw Skills 管理大量技术文档、博客文章或内部沟通(其中清晰度和专业性至关重要)的团队特别有价值。
通过直接集成到您的 AI 智能体工作流中,此技能超越了简单的拼写检查。它评估文档结构,识别被动语态,并确保复杂的技术概念能够有效地传达给目标受众。无论您是在润色 README 还是准备白皮书,此技能都能自动完成编辑过程中最枯燥的部分。
下载入口:https://github.com/openclaw/skills/tree/main/skills/mattvalenta/pls-copy-editing
安装与下载
1. ClawHub CLI
从源直接安装技能的最快方式。
npx clawhub@latest install pls-copy-editing
2. 手动安装
将技能文件夹复制到以下位置之一
全局模式~/.openclaw/skills/
工作区
/skills/
优先级:工作区 > 本地 > 内置
3. 提示词安装
将此提示词复制到 OpenClaw 即可自动安装。
请帮我使用 Clawhub 安装 pls-copy-editing。如果尚未安装 Clawhub,请先安装(npm i -g clawhub)。
文字编辑 应用场景
- 润色技术文档和 API 参考,以提高开发者的清晰度。
- 审计营销材料,确保符合品牌语调和风格指南。
- 在大量文档中标准化格式和术语。
- 为非技术利益相关者简化复杂的语言。
- 校对自动化报告和 AI 生成的草稿,以确保结构完整性。
- AI 智能体摄取原始文本并识别特定的编辑语境(如技术、休闲或正式)。
- 加载参考材料(如风格指南和词汇表),以建立该会话的规则集。
- Python 脚本分析文档的语法准确性、一致性和可读性评分。
- 技能生成文本的修订版本,突出重大更改并提供风格选择的依据。
- 最终输出根据 assets 目录中提供的模板进行格式化。
文字编辑 配置指南
要开始使用 Openclaw Skills 的文字编辑功能,请在项目目录中初始化该技能:
# 安装文字编辑技能
openclaw skill add copy-editing
# 填充您的参考风格指南
cp global-styles.md ./skills/copy-editing/references/style-guide.md
文字编辑 数据架构与分类体系
该技能通过结构化的目录系统组织其编辑逻辑:
| 目录 | 用途 |
|---|---|
references/ |
包含品牌指南、违禁词列表和语法规则的 Markdown 文件。 |
scripts/ |
包含用于基于正则表达式的文本清洗和可读性指标计算的 Python 工具。 |
assets/ |
存储文档模板和样板结构,用于一致的输出格式化。 |
context/ |
临时存储有关当前文档语调和目标受众的元数据。 |
name: copy-editing
description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
Copy Editing
Overview
[TODO: 1-2 sentences explaining what this skill enables]
Structuring This Skill
[TODO: Choose the structure that best fits this skill's purpose. Common patterns:
1. Workflow-Based (best for sequential processes)
- Works well when there are clear step-by-step procedures
- Example: DOCX skill with "Workflow Decision Tree" -> "Reading" -> "Creating" -> "Editing"
- Structure: ## Overview -> ## Workflow Decision Tree -> ## Step 1 -> ## Step 2...
2. Task-Based (best for tool collections)
- Works well when the skill offers different operations/capabilities
- Example: PDF skill with "Quick Start" -> "Merge PDFs" -> "Split PDFs" -> "Extract Text"
- Structure: ## Overview -> ## Quick Start -> ## Task Category 1 -> ## Task Category 2...
3. Reference/Guidelines (best for standards or specifications)
- Works well for brand guidelines, coding standards, or requirements
- Example: Brand styling with "Brand Guidelines" -> "Colors" -> "Typography" -> "Features"
- Structure: ## Overview -> ## Guidelines -> ## Specifications -> ## Usage...
4. Capabilities-Based (best for integrated systems)
- Works well when the skill provides multiple interrelated features
- Example: Product Management with "Core Capabilities" -> numbered capability list
- Structure: ## Overview -> ## Core Capabilities -> ### 1. Feature -> ### 2. Feature...
Patterns can be mixed and matched as needed. Most skills combine patterns (e.g., start with task-based, add workflow for complex operations).
Delete this entire "Structuring This Skill" section when done - it's just guidance.]
[TODO: Replace with the first main section based on chosen structure]
[TODO: Add content here. See examples in existing skills:
- Code samples for technical skills
- Decision trees for complex workflows
- Concrete examples with realistic user requests
- References to scripts/templates/references as needed]
Resources (optional)
Create only the resource directories this skill actually needs. Delete this section if no resources are required.
scripts/
Executable code (Python/Bash/etc.) that can be run directly to perform specific operations.
Examples from other skills:
- PDF skill:
fill_fillable_fields.py,extract_form_field_info.py- utilities for PDF manipulation - DOCX skill:
document.py,utilities.py- Python modules for document processing
Appropriate for: Python scripts, shell scripts, or any executable code that performs automation, data processing, or specific operations.
Note: Scripts may be executed without loading into context, but can still be read by Codex for patching or environment adjustments.
references/
Documentation and reference material intended to be loaded into context to inform Codex's process and thinking.
Examples from other skills:
- Product management:
communication.md,context_building.md- detailed workflow guides - BigQuery: API reference documentation and query examples
- Finance: Schema documentation, company policies
Appropriate for: In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Codex should reference while working.
assets/
Files not intended to be loaded into context, but rather used within the output Codex produces.
Examples from other skills:
- Brand styling: PowerPoint template files (.pptx), logo files
- Frontend builder: HTML/React boilerplate project directories
- Typography: Font files (.ttf, .woff2)
Appropriate for: Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
Not every skill requires all three types of resources.
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