Group Chat Summarizer

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

Quick Start

7w4.net收錄了海量優質技能外掛。

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
• 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。