A technical skill for AI agents to maintain professional visual hierarchy and rich text integrity when programmatically editing Feishu or Lark documents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install feishu-readability
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install feishu-readability using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Feishu Document Readability Optimization?
The Feishu Document Readability Optimization skill is a specialized framework designed to solve common formatting challenges encountered when AI agents edit Lark Docs via API. It focuses on the internal logic of the Feishu Markdown rendering engine, which often collapses whitespace and corrupts rich text attributes during automated updates. By implementing this skill within the ecosystem of Openclaw Skills, developers can ensure that document edits are precise, visually structured, and safe for production environments.
At its core, the skill enforces three major pillars: Minimal Modification, Visual Spacing, and Formatting Hygiene. It moves away from destructive overwrite operations, instead utilizing surgical byte-level positioning to preserve comments, history, and embedded tokens like images or callout blocks. This makes it an essential tool for any workflow involving automated report generation or document management in Feishu.
Feishu Document Readability Optimization Use Cases
- Automating the generation of weekly reports with consistent visual spacing between headers.
- Inserting new content into existing documents without losing existing comments or version history.
- Standardizing typography across shared team documents, including bolding and punctuation rules.
- Preventing formatting collapse when transitioning from list items to new section headers.
- Safely updating documents that contain complex rich text elements like images and callout blocks.
How Feishu Document Readability Optimization Works
- The agent fetches the current document content to diagnose crowding issues and identify unique context tokens for positioning.
- It calculates the minimal modification range required, avoiding the destructive overwrite mode to protect document metadata.
- Visual spacers are injected using non-default paragraph attributes like {align="center"} to force the renderer to maintain vertical gaps.
- Formatting refinements are applied to bold text and list items to ensure proper colon spacing and punctuation hygiene.
- The agent performs a pre-flight check to verify that rich text tokens like images remain intact before committing the update.
Feishu Document Readability Optimization Setup
This skill must be loaded before the AI agent attempts to modify any documents on the mi.feishu.cn domain. It is specifically designed to work alongside Feishu MCP tools.
# Standard trigger conditions:
# 1. User requests modification of Feishu document content
# 2. Domain involves mi.feishu.cn
# 3. Call to update-doc tool is initiated
Feishu Document Readability Optimization Data Schema & Taxonomy
The skill organizes document updates based on the following structural taxonomy:
| Element | Technical Implementation | Purpose |
|---|---|---|
| Minimal Selection | selection_with_ellipsis | Ensures unique positioning with minimal context overhead |
| Visual Spacers | \n\n {align="center"}\n\n | Forces the API to preserve vertical white space between blocks |
| Bold Spacing | Title: Text | Standardizes character-level spacing for colons and bold tags |
| Token Protection | Identifies and preserves non-textual components during edits |
Feishu Document Readability Optimization Advanced Features
- Byte-level precision targeting to modify specific sentences without re-writing entire paragraphs.
- List-break automation that uses double-newline spacers to prevent list formatting from bleeding into headers.
- Context-aware spacer injection that adapts based on whether the preceding element is a list, image, or text.
- Rich text token preservation which ensures that internal Feishu attributes like image tokens are not lost during Markdown processing.
SKILL.md
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