Fetch and convert Feishu Wiki, Docx, Sheets, and Bitable content into Markdown.
npx skills add https://github.com/papachong/RHClaw --skill feishu-doc-papachong
Please help me install this Agent Skill. Skill: feishu-doc Source: https://github.com/papachong/RHClaw/tree/main/RHClaw-Desktop/src-tauri/skills/feishu-doc Command: npx skills add https://github.com/papachong/RHClaw --skill feishu-doc-papachong
Many teams store knowledge across Feishu (Lark) Wiki pages, Docs, Sheets, and Bitable but lack a reliable way to extract, edit, or ingest that content into local editors or LLM workflows; this Skill automates fetching and converting those resources into Markdown and structured blocks so content is reusable and editable.
Use the feishu-doc skill to read a Feishu doc URL or token and convert it into Markdown for editing or LLM input.
High-intent search queries and answers about installing and using this skill.
You can convert Feishu Wiki pages to Markdown by resolving wiki links and fetching document blocks through the Feishu API. This Skill handles tenant token retrieval, paginated block fetching, and outputs structured Markdown for local editing or LLM context.
Yes, you can append Markdown content back into a Feishu Docx. The Skill converts Markdown into Feishu blocks, applies input sanitization to avoid API 400 errors, and appends large documents in safe batched chunks with retry support.
Yes, fetching Bitable records and Sheets requires Feishu app credentials. The Skill uses tenant token retrieval with caching to authenticate API requests, extract sheet or bitable data, and convert it into Markdown format.
The best way to import a Feishu document into an LLM workflow is to fetch and convert Docx or legacy Doc content into Markdown. This Skill handles paginated block retrieval and token management, outputting clean Markdown text ideal for LLM context construction.
Writing Markdown to Feishu blocks often fails with API 400 errors due to unsanitized input or batch size limits. This Skill prevents these errors by applying input sanitization, batching block inserts, and falling back to single-item inserts with retries for large documents.
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