Style Learner analyzes technical and creative documents to extract structural, linguistic, and formatting patterns into reusable style guides.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install writing-style-learner
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 writing-style-learner 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 Style Learner?
Style Learner is a sophisticated tool within the Openclaw Skills ecosystem designed to help AI agents adapt to specific writing standards. By analyzing existing documentation such as technical specs, meeting notes, or requirements, it identifies the unique DNA of a document's style—ranging from heading hierarchies to specific vocabulary choices.
This skill enables teams and individuals to maintain consistency across their digital workspace. It transforms raw document links into structured style records, allowing for the automated generation of content that mirrors the tone and layout of established high-quality work.
Style Learner Use Cases
- Creating a unified team writing guide based on successful project documentation.
- Extracting technical writing patterns from Feishu or Wiki documents to train AI agents.
- Building personal writing profiles for different document types like PRDs or meeting minutes.
- Standardizing code block and table usage across multiple technical repositories.
How Style Learner Works
- The user provides one or more document URLs (e.g., Feishu links) and specifies an optional profile name.
- Style Learner parses the document to analyze specific dimensions such as paragraph organization and high-frequency vocabulary.
- The skill identifies recurring formatting habits, including heading structures and code block preferences.
- The extracted data is processed based on the selected mode (single, batch, or team).
- A standardized style record is generated and saved to the agent's long-term memory for future application.
Style Learner Setup
To integrate this skill into your environment, ensure your agent has access to the target document URLs and run the following command to initialize the Openclaw Skills configuration:
# Example of triggering a learning session
user: Learn this document's style https://xfmkp6q7le.feishu.cn/wiki/xxx -as "Oldtimer"
Style Learner Data Schema & Taxonomy
Style Learner organizes its findings into a structured taxonomy within the memory layer. The following dimensions are captured:
| Dimension | Description |
|---|---|
| Hierarchy | Usage patterns for #, ##, and ### headers. |
| Organization | The logical flow (e.g., Background → Solution → Todo). |
| Syntax | Preferred code block languages (cpp, json, python). |
| Tone | Assessment of casual vs formal language. |
| Vocabulary | Identification of high-frequency keywords and terms. |
| Layout | Whitespace habits, list styles, and table usage. |
Style Learner Advanced Features
- Batch Learning Mode: Analyze multiple sources simultaneously to find common denominators in writing style.
- Team Style Synchronization: Consolidate patterns from various authors into a single, authoritative team style guide.
- Memory Persistence: Automatically saves style records to MEMORY.md or custom locations for persistent cross-session access.
- Dimension Targeting: Specifically focuses on collaboration elements like @mentions and internal references.
SKILL.md
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