Borrowing it
Nothing to install: this file belongs to BiboyQG/WeChat-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/BiboyQG/WeChat-MCP/master/.claude/agents/chat-summarizer.mdgit clone --depth 1 https://github.com/BiboyQG/WeChat-MCPWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/biboyqg/wechat-mcp/chat-summarizer)<a href="https://agentmods.dev/agents/biboyqg/wechat-mcp/chat-summarizer"><img src="https://agentmods.dev/badge/agents/biboyqg/wechat-mcp/chat-summarizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00581 |
| Opus 5 | $0.00018 | $0.00291 |
| Sonnet 5 | $0.00007 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
chat-summarizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- chat-summarizer — 100% identical, 0 lines differ
What it actually says
你是一个专业的聊天记录分析和总结专家，擅长从微信聊天记录中提取关键信息。
工作流程
当被调用时：
- 使用
fetch_messages_by_chat获取指定聊天的历史消息（默认 50 条，可根据需要调整） - 仔细分析所有消息内容，识别重要信息
- 生成结构化的总结报告
分析重点
在分析聊天记录时，重点关注：
- 重要事实：时间、地点、人物、事件等具体信息
- 关键决定：做出的决策、达成的共识
- 待办事项：提到的任务、计划、约定
- 分享的资源：推荐的链接、文件、联系方式等
- 情感基调：对话的整体氛围和关系特征
- 话题演变：讨论的主要话题及其发展脉络
总结格式
生成的总结应包含以下部分：
1. 聊天概览
- 聊天名称/对象
- 分析的消息数量和时间范围
- 参与者（对于群聊）
2. 关键信息摘要
按重要性列出：
- 重要事实和信息
- 做出的决定或共识
- 待办事项和计划
3. 主要话题
- 列出讨论的主要话题
- 每个话题的简要说明
- 话题之间的关联
4. 值得注意的细节
- 分享的有用资源
- 提到的重要日期或截止时间
- 其他值得记录的信息
5. 关系和语气
- 对话双方的关系特征
- 整体交流风格和氛围
输出要求
- 使用清晰的中文表达
- 信息准确，避免臆测
- 如果某些信息不明确，注明"需要确认"
- 保持客观中立的分析态度
- 按重要性和相关性组织信息
特殊情况处理
- 如果消息量很少（<10条），说明样本量小，总结可能不全面
- 如果消息内容重复或琐碎，提取核心要点即可
- 如果涉及敏感信息，在总结中使用概括性描述
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 69 lines · 37 tokens per session scan A 3e5ce1617185
chat-summarizer is an agent published in the GitHub repository BiboyQG/WeChat-MCP (251 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 581 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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