← 返回 Skills 市场
reed1898

Lark Suite Wiki

作者 Reed · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
1620
总下载
1
收藏
5
当前安装
1
版本数
在 OpenClaw 中安装
/install larksuite-wiki
功能描述
Manage and export Lark Suite (Feishu) Wiki/Knowledge Base documents. Read, search, sync with subdocuments, and incremental export to local Markdown files.
使用说明 (SKILL.md)

Lark Suite Wiki

Manage and export Lark Suite (Feishu) Wiki/Knowledge Base documents with recursive sync and incremental updates.

Prerequisites

  1. Create a Lark/Feishu app at https://open.larksuite.com/console
  2. Enable permissions:
    • docs:doc:read
    • drive:drive:read
    • wiki:wiki:read
  3. Publish the app and authorize it to access your wiki
  4. Set environment variables (or edit script defaults): 
    export LARK_APP_ID="cli_xxxxxxxx"
export LARK_APP_SECRET="xxxxxxxx"

Commands

List Wiki Spaces

larksuite-wiki spaces

Read Document (with subdocument links)

larksuite-wiki read \x3Cdoc_id_or_url>

Export Single Document

larksuite-wiki export \x3Cdoc_id_or_url> --output ./docs/

Show Document Tree Structure

larksuite-wiki tree \x3Cdoc_id_or_url>

Sync Entire Wiki (Recursive Export)

# First sync - exports all documents
larksuite-wiki sync \x3Cdoc_id_or_url> --output ./lark-wiki/

# Incremental sync - only exports changed documents
larksuite-wiki sync \x3Cdoc_id_or_url> --output ./lark-wiki/

# Force re-export everything
larksuite-wiki sync \x3Cdoc_id_or_url> --output ./lark-wiki/ --force

Features

1. ✅ Batch Export

Export entire knowledge base with one command.

2. ✅ Recursive Subdocument Export

Automatically follows and exports all linked subdocuments.

3. ✅ Preserves Directory Structure

Creates nested folders matching your wiki structure:

lark-wiki/
├── 01_首页/
│   ├── 01_首页.md
│   └── 01_日常复盘/
│       ├── 01_日常复盘.md
│       └── ...
├── 02_云聪金融分析/
│   └── ...

4. ✅ Incremental Sync

Tracks document revisions and only exports changed documents:

  • Saves sync state to .lark-sync-state.json
  • Compares revision IDs
  • Skips unchanged documents

Quick Start

Export your entire wiki

# Get your wiki root document ID from the URL
# https://xxx.larksuite.com/wiki/TDCZweBJ2iMFO4kI1LAlSE62gnd

# Sync everything
python3 larksuite-wiki.py sync TDCZweBJ2iMFO4kI1LAlSE62gnd --output ./my-wiki/

Daily incremental sync

# Run daily - only exports changed documents
python3 larksuite-wiki.py sync TDCZweBJ2iMFO4kI1LAlSE62gnd --output ./my-wiki/

Output Structure

Each document gets its own folder:

  • Main .md file
  • Subfolders for child documents
  • Numbered prefixes for ordering (01_, 02_, etc.)

API Reference

Notes

  • Documents must be explicitly shared with your app
  • Some block types may not convert perfectly to Markdown
  • Large wikis with many subdocuments may take time to sync
  • Sync state is saved locally for incremental updates
安全使用建议
This tool appears to do what it claims (export Lark/Feishu wiki content), but the shipped script contains embedded Lark app credentials as fallbacks. Before installing or running it: (1) Inspect the code and remove or replace embedded credentials; (2) do not rely on the defaults — set LARK_APP_ID and LARK_APP_SECRET in your environment with an app you control; (3) ensure the app has only the minimal read permissions and is authorized only to the docs you intend to export; (4) run the script in an isolated environment (non-production machine or container) the first time; (5) if you inadvertently used the embedded credentials with your data, consider whether you granted that app access and rotate any affected secrets or app authorizations. The presence of hardcoded secrets is a hygiene/security issue — treat it as suspicious rather than an outright sign of malicious intent.
功能分析
Type: OpenClaw Skill Name: larksuite-wiki Version: 1.0.0 The skill is designed to manage and export Lark Suite Wiki documents. It accesses the Lark Suite API (`open.larksuite.com`) using credentials provided via environment variables (`LARK_APP_ID`, `LARK_APP_SECRET`) or hardcoded example values. File system operations are limited to creating a user-specified output directory and writing Markdown files and a local sync state file (`.lark-sync-state.json`) within it. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, or prompt injection attempts in `SKILL.md` or `README.md`. The hardcoded example credentials in `larksuite-wiki.py` and `references/api-reference.md` are not considered malicious, as they appear to be public example client IDs/secrets for a generic CLI app and are intended to be overridden by user-provided environment variables.
能力评估
⚠ Purpose & Capability
The skill's name, description, commands, and required env vars (LARK_APP_ID, LARK_APP_SECRET) align with a Lark Wiki export tool. However, the included Python file embeds fallback hardcoded credentials (app_id and app_secret) inside the code, which is not necessary for the stated purpose and contradicts the declared requirement to provide env vars.
⚠ Instruction Scope
SKILL.md instructs the agent/user to create a Lark app, set env vars, and authorize access — all appropriate. The runtime instructions and included code only call Lark/Open API endpoints and write exported Markdown and a local .lark-sync-state.json file. The problem: the code will silently use embedded default credentials if env vars are not set, which the instructions do not mention. There are no instructions to read unrelated system files or to contact third-party endpoints outside open.larksuite.com.
✓ Install Mechanism
This is instruction-only with a Python script; there is no install spec, no downloads, and no archive extraction. Risk from installation is low because nothing is written by an automated installer beyond the user running the script.
⚠ Credentials
Only two environment variables are requested (LARK_APP_ID, LARK_APP_SECRET), which is proportionate for a Lark API client. However, the code includes hardcoded app_id and app_secret fallback values. Embedding credentials in the repository is unnecessary and may leak the maintainer's secret or cause the tool to operate under an unexpected identity if env vars are missing.
✓ Persistence & Privilege
The skill does not request persistent platform-level privileges (always: false). It saves a local .lark-sync-state.json file for incremental sync, which is consistent with the stated behavior and scoped to the output directory. The skill does not modify other skills or system-wide agent configurations.
如何使用
  1. 确保已安装 OpenClaw（本地或 Docker 部署）
  2. 在对话框中输入安装命令：/install larksuite-wiki
  3. 安装完成后，直接呼叫该 Skill 的名称或使用 /larksuite-wiki 触发
  4. 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
Slug larksuite-wiki
版本 1.0.0
许可证 —
累计安装 5
当前安装数 5
历史版本数 1
常见问题

Lark Suite Wiki 是什么？

Manage and export Lark Suite (Feishu) Wiki/Knowledge Base documents. Read, search, sync with subdocuments, and incremental export to local Markdown files. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 1620 次。

如何安装 Lark Suite Wiki？

在 OpenClaw 或 Claude Code 对话框中运行命令「/install larksuite-wiki」即可一键安装，无需额外配置。

Lark Suite Wiki 是免费的吗？

是的，Lark Suite Wiki 完全免费（开源免费），可自由下载、安装和使用。

Lark Suite Wiki 支持哪些平台？

Lark Suite Wiki 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。

谁开发了 Lark Suite Wiki？

由 Reed（@reed1898）开发并维护，当前版本 v1.0.0。

推荐 Skills
self-improving agent
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
⬇ 463283 · by pskoett
Skill Vetter
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
⬇ 259410 · by spclaudehome
Polymarket
Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.
⬇ 232503 · by joelchance
Self-Improving + Proactive Agent
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
⬇ 200423 · by ivangdavila
Github
Interact with GitHub using the `gh` CLI. Use `gh issue`, `gh pr`, `gh run`, and `gh api` for issues, PRs, CI runs, and advanced queries.
⬇ 191115 · by steipete
ontology
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
⬇ 190156 · by oswalpalash

💬 留言讨论