Manage Feishu cloud documents via command-line interface.
npx skills add https://github.com/qiushibang/skill-creator-pro --skill lark-doc-qiushibang
Please help me install this Agent Skill. Skill: lark-doc Source: https://github.com/qiushibang/skill-creator-pro/tree/main/references/lark-doc Command: npx skills add https://github.com/qiushibang/skill-creator-pro --skill lark-doc-qiushibang
This Skill eliminates the manual complexity of managing Feishu (Lark) cloud documents by providing a unified command-line interface for creation, retrieval, updating, and search operations. It handles the intricacies of different document types, wiki token resolution, and media management, enabling users to focus on content rather than API details.
Use the lark-doc skill to create a new Feishu document from Markdown content, insert an image, and search for an existing spreadsheet by name in your cloud space.
High-intent search queries and answers about installing and using this skill.
To automate Feishu document creation from Markdown, use a command-line interface that converts Markdown content into cloud documents via Feishu OpenAPIs, handling the API intricacies automatically.
Yes, you can insert images and media into Feishu cloud documents by using command-line operations designed for media handling, which also support downloading assets and creating blank whiteboards.
The best way to search for a spreadsheet in Feishu cloud space is using keyword queries with time-based filtering, allowing intelligent resource discovery across documents, wiki pages, and spreadsheets.
Yes, updating Feishu wiki pages requires valid authentication credentials and resolving the knowledge base wiki token to the actual document token to handle nested knowledge spaces correctly.
A limitation of using a CLI for Feishu document management is the strict dependency on the lark-cli binary and valid authentication credentials to interact with Feishu OpenAPIs for all operations.
Wiki token resolution works by automatically converting knowledge base wiki tokens into actual document tokens and types, abstracting the complexity of nested knowledge spaces for seamless retrieval.
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