wechat-daily-report▌
advisorydz/wechat-daily-report-skill · updated Apr 8, 2026
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⚠️ 最终输出是 PNG 图片,不是 HTML。确保 --output 参数使用 .png 后缀。
微信群聊日报生成 Skill
工作流程
1. 运行 analyze_chat.py 分析聊天记录
↓
2. AI 根据聊天文本生成内容 (ai_content.json)
↓
3. 运行 generate_report.py 生成日报图片 (.png)
⚠️ 最终输出是 PNG 图片,不是 HTML。确保 --output 参数使用
.png后缀。
使用步骤
1. 分析聊天记录
python scripts/analyze_chat.py <聊天记录.json> --output-stats stats.json --output-text simplified_chat.txt
输出:
stats.json- 统计数据(话唠榜、熬夜冠军、词云等)simplified_chat.txt- 压缩格式聊天文本,供 AI 分析- 格式:
[时间段] 发言人A:内容 | 发言人B:内容/内容/内容 |分隔不同发言人,/分隔同一人连续消息- 如消息量大(超过1800行),自动拆分为
simplified_chat_1.txt,simplified_chat_2.txt等多个文件
- 格式:
2. AI 生成内容
根据 simplified_chat.txt(或多个分块文件,见 stats.json 中的 raw_text_paths 字段)和 stats.json,按 references/ai_prompt.md 中的格式生成 AI 内容,保存为 ai_content.json。
AI 需要生成的内容包括:
topics: 讨论热点(3-5 个)resources: 教程/资源分享important_messages: 重要消息dialogues: 有趣对话qas: 问答talker_profiles: 话唠成员的特点标签(常用词已由脚本统计)
3. 生成日报图片
python scripts/generate_report.py --stats stats.json --ai-content ai_content.json --output report.png --clean-temp
✅ 输出后缀必须是
.png,使用 iPhone 14 Pro Max 分辨率 (430x932 @3x)使用
--clean-temp参数可在生成完成后自动删除临时文件(stats.json、ai_content.json、simplified_chat.txt、临时 HTML)图片生成需要安装 playwright:
pip install playwright && playwright install chromium
聊天记录 JSON 格式
{
"meta": {
"name": "群名称",
"platform": "wechat",
"type": "group"
},
"members": [
{"platformId": "xxx", "accountName": "昵称", "groupNickname": "群昵称"}
],
"messages": [
{
"sender": "platformId",
"accountName": "昵称",
"groupNickname": "群昵称",
"timestamp": 1234567890,
"type": 0,
"content": "消息内容"
}
]
}
💡 显示名称优先级:
groupNickname>accountName
消息类型 (type):
0: 纯文本(脚本和 AI 分析此类型)1: 图片2: 语音转文字(脚本和 AI 分析此类型,内容格式:[语音转文字] xxx)5: 动画表情99: 系统消息
脚本分析内容(保证准确性)
| 数据 | 说明 |
|---|---|
| 总消息数 | 所有消息计数 |
| 活跃用户数 | 去重用户数 |
| 时间范围 | 首尾消息时间 |
| 话唠榜 TOP3 | 按发言数排序 + 常用词 |
| 熬夜冠军 | 23:00-06:00 最晚活跃者 |
| 词云数据 | jieba 分词 + 词频统计 |
AI 生成内容(需要理解上下文)
| 内容 | 输入数据 |
|---|---|
| 讨论热点 | 精简文本 + 词云 TOP50 |
| 成员画像 | 精简文本中的发言 |
| 有趣对话 | 高互动片段 |
| 问答识别 | 问号消息 ± 上下文 |
| 教程/资源 | 精简全文识别 |
依赖
pip install jieba jinja2 playwright
playwright install chromium
How to use wechat-daily-report on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add wechat-daily-report
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches wechat-daily-report from GitHub repository advisorydz/wechat-daily-report-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate wechat-daily-report. Access the skill through slash commands (e.g., /wechat-daily-report) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★52 reviews- ★★★★★Kofi Sharma· Dec 28, 2024
wechat-daily-report reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Nia Gupta· Dec 28, 2024
Keeps context tight: wechat-daily-report is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Anaya Farah· Dec 4, 2024
I recommend wechat-daily-report for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ava Sanchez· Nov 23, 2024
Registry listing for wechat-daily-report matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Kim· Nov 23, 2024
Solid pick for teams standardizing on skills: wechat-daily-report is focused, and the summary matches what you get after install.
- ★★★★★Omar Tandon· Nov 19, 2024
wechat-daily-report has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Omar Verma· Nov 19, 2024
wechat-daily-report is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Anika Menon· Nov 3, 2024
Useful defaults in wechat-daily-report — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Carlos Rao· Oct 22, 2024
Registry listing for wechat-daily-report matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Benjamin Ndlovu· Oct 14, 2024
Useful defaults in wechat-daily-report — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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