Automate Lark document, chat, and Base/Bitable interactions via a shell-based API client.
npx skills add https://github.com/popodidi/harvis --skill lark-popodidi
Please help me install this Agent Skill. Skill: lark Source: https://github.com/popodidi/harvis/tree/main/projects/truenorth/skills/lark Command: npx skills add https://github.com/popodidi/harvis --skill lark-popodidi
Organizations using Lark/Feishu often work with documents, chats, and directory data across multiple apps. This skill provides a unified, scriptable interface to read and edit documents, send messages, look up users, and query Base/Bitable data from Lark.
Source the authentication script and obtain a token with get_lark_token. Retrieve a document's blocks with get_document_blocks or get_all_document_blocks. Use the provided MCP tools or the lark_api helper to perform actions such as reading content, sending messages, or looking up users.
High-intent search queries and answers about installing and using this skill.
You can automate sending Lark messages and reading documents by using a scriptable shell-based API client that manages tokens and interacts with Lark Base data, chats, and docs. It supports looking up users and extracting document content for pipelines.
Yes, curl and jq are required dependencies to query Lark Base data and look up users by email. The skill uses these shell tools to handle API requests, parse JSON responses, and manage local token caching with retry logic.
The best way to manage Feishu authentication tokens for API automation is through a dedicated script that handles token retrieval and local token caching with retry logic. You source the authentication script and call get_lark_token to obtain a valid token.
To extract document blocks from Lark docs for downstream processing, you use the get_document_blocks or get_all_document_blocks functions. These retrieve the structural content of your Lark documents, allowing you to read and edit the information within your automation pipeline.
Yes, Lark API automation works with Bitable data for team notifications by querying Lark Base records and sending messages to chats. You can integrate these actions to automatically notify teams or update wiki notes based on your Bitable data.
Git and bc are required dependencies to run Lark integration scripts because git likely handles script versioning or updates, while bc provides shell-based calculation logic needed for the API client operations. They ensure the local token caching and retry logic function correctly.
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