Feishu Skill - AI Tool Extension for Feishu API

A progressively extensible skill designed for AI tools to leverage Feishu capabilities. The core philosophy is implement only what you need, when you need it.

⚠️ CRITICAL: After Creating New Scripts

You MUST update scripts/script_index.json immediately after creating any new script.

Failure to update the index will cause:

• - Future AI lookup cannot find the newly created script
• Duplicate implementation of the same functionality
• Confusion for other AI tools using this skill

Action required: Add an entry to scripts/script_index.json with fields: name, description, usage_hint, added_date, INLINECODE6

Design Philosophy

This skill is designed specifically for AI tools, not for human manual use.

When an AI tool receives a task involving Feishu:

1. 1. Check existing scripts first - Search scripts/script_index.json for already-implemented APIs
2. Implement only if needed - If no match exists, use Context7 to query Feishu OpenAPI documentation
3. Add to index - Once implemented, update the index to avoid future re-implementation

This approach ensures:

• - No wasted effort - Only implement APIs when actually needed
• Progressive expansion - The skill grows based on real usage patterns
• Efficient lookup - AI tools can quickly find what's already available

Quick Start

This skill works out of the box once authentication is configured:

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AI Workflow

Decision Logic

When AI encounters a Feishu-related task, follow this decision tree:

Step 1: Check index file
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If match found:

• - Run python3 script.py --help to understand parameters
• Execute the script
• Return result

If NO match found:

• - Use Context7 to query Feishu API documentation:

  Library: open.feishu.cn / larksuite
  Query: "[specific requirement, e.g., 如何获取群组成员列表]"
  

• - Implement new Python script based on documentation
• Execute and test
• IMPORTANT: Update scripts/script_index.json to add the new script

If execution fails:

• - Check API error messages
• Use Context7 or references/doc_urls.txt to consult documentation
• Fix the script
• Re-test

Critical Decision Points

When deciding between "use existing" vs "implement new", consider:

• - Is there a semantic match? Even if parameter names differ, if the functionality is equivalent, use existing script
• What's the cost of implementing? Simple single-call APIs → implement immediately. Complex multi-call workflows → consider reusing existing scripts
• Can this compose from existing scripts? Sometimes new features can be achieved by combining multiple existing scripts

Technical Reference

Script Index Format

INLINECODE11 structure:

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Key fields for AI search:

• - description: Human-readable explanation of functionality
• INLINECODE13: Array of keywords for matching (prioritize these for fuzzy matching)

Authentication

Configuration file: INLINECODE14

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To obtain credentials:

1. 1. Login to Feishu Open Platform
2. Create or select an application
3. Get App ID and App Secret from application details

Token management: get_token.py handles token lifecycle:

• - Automatic caching during validity period
• Auto-refresh 5 minutes before expiration
• Cache file: INLINECODE16

Script Template

When implementing new APIs, use this template:

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Development standards:

1. 1. ⚠️ CRITICAL: After creating any new script, you MUST update scripts/script_index.json immediately
2. Complete docstring with Usage, Parameters, Options, Output sections
3. Support --help parameter
4. Use get_token.py for authentication
5. Output standard JSON format
6. Proper error handling with friendly messages

📋 New Script Creation Checklist

When implementing a new API, you MUST complete ALL steps:

• - [ ] Query Context7 for API documentation
• [ ] Create the Python script following the template
• [ ] Test the script execution (verify it returns correct JSON)
• [ ] ⚠️ UPDATE scripts/script_index.json ← Don't forget this!
• [ ] Save the API documentation URL to INLINECODE21

Using Context7 for Feishu API

When implementing new APIs, query Feishu documentation via Context7:

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Why Context7?

• - Provides up-to-date API documentation
• Includes code examples and parameters
• Faster than searching the web manually
• Reduces trial-and-error

Examples

AI Task Example 1: Get Group Members

User request: "Get the member list for group oc_xxxxxxxxxxxxx"

AI decision process:

1. 1. Search scripts/script_index.json → Found match: INLINECODE23
2. Execute: INLINECODE24
3. Return result

AI Task Example 2: Add New API Capability

User request: "Delete a message from the group"

AI decision process:

1. 1. Search scripts/script_index.json → No match
2. Context7 query: "飞书删除消息 API"
3. Get documentation showing message/delete endpoint
4. Implement delete_message.py script
5. Test execution
6. ⛔ CRITICAL STEP: Update scripts/script_index.json with new entry - DO NOT skip this!
7. Return result

Why step 6 is critical:

• - If you skip it, next AI will NOT find delete_message.py in the index
• This causes duplicate work: same functionality will be re-implemented
• The progressive expansion philosophy breaks down

References

• - Feishu Open Platform
• Feishu API Documentation
• DEVELOPMENT.md - Detailed development guidelines