An automated workflow for Feishu/Lark documents that reads unresolved comments and applies targeted block-by-block edits.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lark-doc-reviser
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 lark-doc-reviser 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 Lark Doc Reviser?
The Lark Doc Reviser is a specialized technical tool within the Openclaw Skills ecosystem designed to streamline the editorial workflow in Feishu (Lark) documents. It bridges the gap between human feedback and automated document updates by reading unresolved comments and providing a programmatic way to apply text changes block-by-block.
This skill is essential for teams who manage high volumes of collaborative content and want to ensure that every piece of feedback is accurately addressed without manual copy-pasting. By utilizing the lark-cli and Python-based scripts, it allows for precise manipulation of document elements while maintaining a clear record of resolved and unresolved tasks.
Lark Doc Reviser Use Cases
- Reviewing and applying editorial feedback in large Feishu documents efficiently.
- Automating document revisions based on comments from multiple stakeholders to save time.
- Synchronizing document states after intensive collaborative review sessions.
- Programmatically resolving addressed feedback to maintain a clean document history using Openclaw Skills.
How Lark Doc Reviser Works
- Fetch the document state and comments using a doc URL or token to generate a local JSON state file in the workspace.
- Review the extracted commented_blocks to identify specific blocks requiring revision and their associated instructions.
- Construct a patches JSON file that defines the new content and formatting (bold, italic, code, etc.) for the target blocks.
- Run the patch script to update the document, which replaces the existing block content with the new elements.
- Mark the addressed comments as resolved via the API and refresh the local state for further iterations.
Lark Doc Reviser Setup
To get started with this addition to your Openclaw Skills library, ensure you have the lark-cli installed and authenticated. Then, you can use the following scripts to interact with your documents:
# Fetch the initial document state and save to workspace
python3 scripts/fetch_doc.py <doc_url_or_token> --out workspace/<token>_state.json
# Apply patches to the document blocks
python3 scripts/patch_blocks.py <doc_token> patches.json
# Resolve addressed comments
python3 scripts/resolve_comments.py <doc_token> <comment_id>
Lark Doc Reviser Data Schema & Taxonomy
The skill manages document data through a state JSON file and a patching system to maintain document integrity within the Openclaw Skills framework.
| Data Component | Description |
|---|---|
commented_blocks |
Includes blocks with unresolved comments, anchor text, and specific editorial instructions. |
all_blocks |
A full list of document blocks used for structural reference during the editing process. |
patches.json |
A structure containing block_id and elements (text, styling fields) used to replace existing content. |
Supported Styles |
Supports text elements with bold, italic, code, strikethrough, and underline attributes. |
Lark Doc Reviser Advanced Features
- Multi-session editing support by maintaining persistent workspace state files across different processing phases.
- Granular text styling control via JSON patches, allowing for complex formatting updates programmatically.
- Batch comment resolution via stdin for high-throughput workflows involving multiple feedback points.
- Fallback mechanisms for structural modifications (like creating or moving blocks) through direct lark-cli integration.
SKILL.md
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