把自然语言描述转为结构化 JSON,并由 mcp-diagram-generator MCP 服务生成 Draw.io、Mermaid 或 Excalidraw 图表文件。
编程
Conversa Analyzer
试用Analyze conversation transcripts: decisions, action items, dates, people, executive summary.
它能做什么
Extract structured insights from conversation transcripts (WhatsApp, email threads, meeting notes, chat logs).
技能文档
Conversation Analyzer
Extract structured insights from conversation transcripts (WhatsApp, email threads, meeting notes, chat logs).
Trigger
Invoke when the user shares a conversation transcript or asks "analyze this conversation", "extract decisions", "what came out of this chat".
Workflow
- Accept the conversation text (from user message, pasted text, or file).
- Parse and analyze for each category below.
- Output a clean structured report.
Output Structure
📝 Executive Summary
3-5 lines: what was discussed, what was decided, key outcome.
📌 Decisions Made
- What was decided
- Who was involved
- Context/constraints
✅ Action Items
| # | Action | Owner | Deadline | Status |
|---|---|---|---|---|
| 1 | ... | ... | ... | Pending |
📅 Dates & Commitments
- Event/commitment → Date → Who committed
👤 People Involved
- Name: Role in conversation, responsibilities mentioned
⚠️ Open Questions / Risks
Anything unresolved, ambiguous, or risky.
Notes
- If the input is very long (>10000 chars), summarize first then extract.
- If no conversation context is provided, ask the user to provide the text or point to a file.
- Use bullet lists, not tables, for WhatsApp-friendly output (no markdown tables).
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