专业文字编辑与内容润色 - Openclaw Skills

作者:互联网

2026-03-29

AI教程

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。
文字编辑 工作原理
  1. 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)。
  2. 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。
  3. Python 脚本分析文档的语法准确性、一致性和可读性评分。
  4. 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。
  5. 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.