feishu-analyst

name	feishu-analyst
description	Intelligent interaction with Feishu/Lark documents via Feishu MCP. Use when analyzing, extracting content from, or querying Feishu documents (.docx, /wiki/ URLs) including text extraction, markdown conversion, table data analysis, and document search. Uses zero-context MCP calling via executor.py.

Feishu Document Analyst

Analyze Feishu documents efficiently with zero-context MCP calling.

Zero-Context MCP 调用

本 Skill 使用 零上下文模式 调用飞书 MCP 工具：

• 工具定义不加载到 context（节省 ~15,000 tokens）
• 工具响应不注入 context（执行时 0 tokens）
• 通过 executor.py 外部执行

Prerequisites

依赖已内置，只需确保：

• Python 3.8+
• mcp package 已安装：pip install mcp

首次使用配置

方式一：运行配置脚本（推荐）

python scripts/setup.py

方式二：设置环境变量

export FEISHU_APP_ID='your-app-id'
export FEISHU_APP_SECRET='your-app-secret'
export FEISHU_AUTH_TYPE='tenant'  # 或 'user'

获取凭证：https://open.feishu.cn/ → 创建应用 → 凭证与基础信息

Quick Start

For PRD Analysis (Recommended)

Use the dedicated slash command: /feishu-prd-analyse <URL>

Example:

/feishu-prd-analyse https://dy3m1s1v7v.feishu.cn/docx/CgMCdRMh8oMtDKxVcURcrb0DnVr

This command will automatically:

1. Read the ENTIRE document (all text, tables, whiteboards, images, diagrams, flowcharts, etc.)
2. Use zero-context MCP calling via executor.py
3. Load the PRD checklist from references/prd_checklist.md
4. Extract document content and convert to Markdown
5. Apply systematic analysis framework
6. Generate structured review with findings and recommendations

Manual Analysis

入口选择指南

场景	入口	说明
固定流程（PRD 分析）	/feishu-prd-analyse	稳定、可预测
动态需求	Python API	灵活、可组合
命令行操作	CLI	直接调用

1. CLI 命令

# 获取文档并保存到临时文件
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" save "https://xxx.feishu.cn/wiki/xxx"

# 输出示例：
# {
#   "document_id": "G85TdPCcTo91CYx4aYzcLKCnnFe",
#   "title": "文档标题",
#   "blocks_file": "/tmp/document_G85TdPCcTo91CYx4aYzcLKCnnFe_blocks.json",
#   "markdown_file": "/tmp/document_G85TdPCcTo91CYx4aYzcLKCnnFe.md"
# }

# 指定输出目录
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" save "https://xxx.feishu.cn/wiki/xxx" --output-dir ./output

# 获取文档大纲
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" outline /tmp/document_xxx_blocks.json

# 列出所有工具
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" list

# 获取工具描述
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" describe get_feishu_document_blocks

# 直接调用工具
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" call get_feishu_document_info \
  --args '{"documentId": "https://xxx.feishu.cn/wiki/xxx", "documentType": "wiki"}'

2. Python API（动态场景推荐）

import sys
sys.path.insert(0, "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts")

from client import FeishuMCPClient
from document import save_document, get_outline

# 保存文档
result = save_document("https://xxx.feishu.cn/wiki/xxx")
print(f"Markdown 文件: {result['markdown_file']}")

# 提取目录
outline = get_outline(result["blocks_file"])

# 底层调用
client = FeishuMCPClient()
info = client.call("get_feishu_document_info", {"documentId": "xxx"})

⚠️ Best Practices

CRITICAL: Use cli.py as Entry Point

CLI 操作应通过 cli.py 进行：

✅ DO - 使用 cli.py save 获取文档 ✅ DO - 使用 cli.py outline 提取大纲 ✅ DO - 使用 Python API 进行动态操作 ❌ DON'T - 直接调用底层 Python 模块

Why?

• 内置脚本处理 47 种块类型（自定义解析器会遗漏很多）
• 包含 错误处理 和边界情况处理
• 持续维护 和 bug 修复
• 零上下文设计，节省 tokens

可用工具

查看完整列表：

python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" list

常用工具：

• get_feishu_document_info - 获取文档信息（支持 wiki/docx）
• get_feishu_document_blocks - 获取文档块结构
• search_feishu_documents - 搜索文档
• batch_create_feishu_blocks - 批量创建块
• get_feishu_whiteboard_content - 获取白板内容
• update_feishu_block_text - 更新块文本
• delete_feishu_document_blocks - 删除块
• get_feishu_image_resource - 获取图片资源
• upload_and_bind_image_to_block - 上传图片
• create_feishu_table - 创建表格
• fill_whiteboard_with_plantuml - 用 PlantUML 填充白板
• get_feishu_root_folder_info - 获取根文件夹信息
• get_feishu_folder_files - 获取文件夹文件列表
• create_feishu_folder - 创建文件夹

Available Scripts

用户入口脚本（位于 scripts/）：

Script	Purpose	CLI Usage
cli.py	CLI 入口：获取文档、提取大纲、调用工具	save URL / outline FILE / call TOOL
setup.py	交互式凭证配置	直接运行

Python API（动态场景推荐）：

from client import FeishuMCPClient
from document import save_document, get_outline

Token Efficiency

Size	Direct MCP Load	Zero-Context + File	Savings
50KB	~15K tokens	~1K tokens	93%
177KB	~54K tokens	~2K tokens	96%
684KB	~210K tokens	~2.5K tokens	98.8%

ALWAYS save large responses (>10KB) to file before processing.

Analysis Approaches

For PRD Analysis (Product Requirements)

Use the /feishu-prd-analyse command:

/feishu-prd-analyse <URL>

When invoked, the command will:

1. Extract document ID from URL (docx or wiki)
2. Fetch and read the ENTIRE document - all text blocks, tables, whiteboards, images, diagrams, flowcharts, code blocks, and embedded content
3. Use zero-context MCP calling via executor.py
4. Automatically load prd_checklist.md framework
5. Apply systematic analysis across 4 dimensions:
   • Ambiguity Check: Vague terms, metrics, timelines
   • Logic Consistency: Contradictions, edge cases, preconditions
   • Data Integrity: Type definitions, constraints, required fields
   • Completeness: User stories, acceptance criteria, success metrics
6. Generate structured review using output template

For General Text Analysis (wikis, notes)

使用 cli.py save 和 cli.py outline 命令：

# 1. 获取文档并保存到临时文件
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" save "https://xxx.feishu.cn/wiki/xxx"

# 2. 获取文档大纲
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" outline /tmp/document_xxx_blocks.json

For Data Querying (tables, schedules, lists)

# 1. 获取文档并保存到临时文件
python "${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts/cli.py" save "https://xxx.feishu.cn/wiki/xxx"

# 2. 使用 Python API 分析数据
python -c "
import sys
sys.path.insert(0, '${CLAUDE_PLUGIN_ROOT}/skills/feishu-analyst/scripts')
from document import get_outline
print(get_outline('/tmp/document_xxx_blocks.json'))
"

Error Handling

• Credentials not configured: Run python scripts/setup.py or set environment variables
• mcp package not installed: pip install mcp
• Permission Denied: Check document access or bot visibility
• API Errors: Verify credentials in ~/.feishu-mcp/config.json
• Authorization Required: User must authorize via browser (user mode)

Slash Command for PRD Analysis

A dedicated slash command /feishu-prd-analyse is available for PRD analysis:

/feishu-prd-analyse <feishu_document_url>

Examples

Analyze a PRD Document:

/feishu-prd-analyse https://dy3m1s1v7v.feishu.cn/docx/xxx

Analyze a Wiki PRD:

/feishu-prd-analyse https://xxx.feishu.cn/wiki/xxxxx

What happens when invoked:

1. Command extracts document ID from URL
2. Reads the COMPLETE document - all text, tables, whiteboards, images, flowcharts, and diagrams
3. Uses zero-context MCP calling via executor.py
4. Automatically loads prd_checklist.md from references/
5. Applies systematic analysis framework
6. Returns structured review with:
   • Executive summary
   • Critical findings (ambiguities, contradictions, data issues, gaps)
   • Questions for product team
   • Recommendations
   • Overall assessment (Ready/Needs Revision/Major Gaps)

Tips

• Use /feishu-prd-analyse for PRD analysis - it automatically applies the checklist
• For other document types, use the skill directly with your specific requirements
• Works with both /docx/ and /wiki/ URLs

Reference Documentation

• prd_checklist.md - PRD analysis checklist