name: group-chat-summarizer description: Intelligent group chat summarization for work and interest groups across all platforms (Feishu, DingTalk, WeChat Work, Discord, Slack, etc.). Extracts key information, action items, topic threads, and generates structured summary reports. Use when users need to summarize group chat conversations, extract to-do items, identify decisions made, or track discussion progress. Supports both manual on-demand summaries and automated daily summaries.

Group Chat Summarizer

Intelligent group chat summarization that transforms messy conversations into actionable insights.

Quick Start

Basic Usage

User: "Summarize this chat log"
[User pastes chat log]
→ Generate standard summary (Basic/Standard/Detailed based on length)

With Options

User: "Give me a detailed summary of yesterday's team chat"
→ Use detailed format with full timeline

User: "Extract only action items from this discussion"
→ Skip narrative, output only action items table

User: "What's the sentiment of this conversation?"
→ Include sentiment analysis section

Supported Platforms

The skill automatically detects and parses: - China: Feishu (飛書), DingTalk (釘釘), WeChat Work (企業微信), QQ - International: Discord, Slack, Microsoft Teams, Telegram - Generic: Plain text, CSV exports, JSON logs

See references/platform_formats.md for format details.

Summary Levels

Basic (幾句話)

• Best for: Quick catch-up, 50-100 messages
• Output: 3-5 bullet points of key decisions

Standard (幾個要點)

• Best for: Daily standups, 100-300 messages
• Output: Topic threads + key decisions + action items

Detailed (完整脈絡)

• Best for: Important meetings, 300+ messages
• Output: Full timeline + all sections + risk analysis

Output Structure

All summaries follow this structure (see references/output_template.md):

1. Basic Info - Group name, time range, participants, message count
2. Topic Timeline - Chronological thread of discussion topics
3. Key Points - Decisions made, risks, important information
4. Action Items - Tasks with owner, deadline, status
5. Follow-up Suggestions - Recommended next steps
6. Notes - Special mentions, absences, reminders

Special Features

Action Item Extraction

Automatically identifies: - Task descriptions - @mentioned owners - Deadline phrases ("by Friday", "next week", "ASAP") - Status indicators ("done", "pending", "blocked")

Sentiment Analysis

Detects conversation tone: - 😊 Positive - collaborative, supportive - 😐 Neutral - factual, informational
- 😟 Negative - conflicts, complaints, concerns - ⚠️ Controversial - disagreements, unresolved debates

Risk & Controversy Detection

Identifies: - Blocked items or impediments - Resource constraints mentioned - Disagreements without resolution - Missing information or dependencies

Workflow

1. Parse - Detect platform format and extract messages
2. Analyze - Identify topics, participants, timeline
3. Extract - Pull out decisions, action items, key info
4. Generate - Create structured summary
5. Enhance - Add sentiment, risks, follow-ups

Platform-Specific Notes

Feishu/DingTalk/WeChat Work

• Supports exported chat logs
• Handles Chinese date/time formats
• Recognizes @mentions and reply threads

Discord/Slack

• Supports JSON exports
• Handles threaded conversations
• Recognizes emoji reactions as sentiment signals

API Integration

When platform APIs are available: - Use scripts/fetch_messages.py to retrieve chat history - Requires appropriate authentication tokens - Respects rate limits and privacy settings

Examples

Example 1: Work Group Daily Summary

Input: 127 messages from product-tech team
Output: 
- 3 topics discussed (Q2 planning, UI review, technical concerns)
- 4 action items identified with owners
- 1 risk flagged (frontend timeline)
- Follow-up: Interface doc due Thursday

Example 2: Interest Group Discussion

Input: 89 messages about weekend hiking plan
Output:
- Topic: Hiking route selection → Decision: Xiangshan Trail
- 5 participants confirmed
- Action: @Alice to book bus by Wednesday
- Note: @Bob unavailable this weekend

Best Practices

1. For long chats (>500 messages): Suggest breaking into time periods
2. For sensitive content: Remind users about privacy when sharing logs
3. For action items: Always verify @mentions are correctly assigned
4. For follow-ups: Suggest specific dates based on context, not generic

Limitations

• Cannot access chats without user-provided logs or API tokens
• May miss context from edited/deleted messages
• Complex threaded discussions may need manual clarification
• Very long messages (>2000 chars) may be truncated in analysis

References

• Output Template - Complete output format specification
• Platform Formats - Supported chat log formats

  小蔥技能站7w4.net，專業的AI技能分享平臺。

• Prompts - LLM prompts for extraction and analysis

這個群聊摘要工具質量不錯，能自動識別飛書、釘釘、企業微信等多個平臺的訊息格式，提取待辦事項、情感傾向和潛在風險，輸出結構清晰的總結報告。優點是功能全面、格式規範、使用方便；不足是部分平臺只支援 JSON 匯出而非文本，某些複雜對話的解析準確性還有提升空間。總體而言是一個實用且完成度較高的生產力工具。