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Weekly Report To Annual

Community
LingyiChen-AI
weekly-report-to-annual

从飞书邮箱读取周报邮件,根据年度报告模板生成年度总结报告

Overview

PublisherLingyiChen-AI
RepositoryOpenSkills
Skill nameweekly-report-to-annual
Stars
69
Forks
5
Bundled files
5
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LingyiChen-AI on GitHub. Read the source before you install it.

Installation

Install the Weekly Report To Annual AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LingyiChen-AI/OpenSkills.git /tmp/OpenSkills
mkdir -p .claude/skills
cp -r /tmp/OpenSkills/examples/weekly-report-to-annual .claude/skills/weekly-report-to-annual
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weekly Report To Annual in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Weekly Report To Annual on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Weekly Report To Annual is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

周报年报生成 Skill

你是一个专业的年度报告生成助手。你可以从用户的飞书邮箱中读取周报邮件,然后根据年度报告模板生成结构化的年度总结。

功能

  1. 读取周报邮件: 通过IMAP协议连接飞书邮箱,筛选标题包含"周报"的邮件
  2. 分析周报内容: 提取周报中的关键信息,包括工作成果、问题和计划
  3. 生成年度报告: 根据模板将周报内容汇总为年度报告
  4. 保存报告: 将生成的报告保存到本地

使用流程

  1. 用户提供飞书邮箱账号信息(邮箱地址和应用密码)
  2. 使用 fetch_emails 脚本读取周报邮件
  3. 分析邮件内容,提取关键信息
  4. 参考年度报告模板(references/annual-report-template.md)
  5. 生成结构化的年度报告
  6. 使用 save_report 脚本保存到本地

飞书邮箱配置说明

飞书邮箱 IMAP 服务器配置:

  • IMAP服务器: imap.feishu.cn
  • 端口: 993 (SSL)
  • 需要在飞书管理后台开启IMAP服务并生成应用密码

注意事项

  1. 请确保已在飞书管理后台开启IMAP服务
  2. 使用应用密码而非登录密码
  3. 邮件读取可能需要一定时间,请耐心等待
  4. 生成的报告会保存为 Markdown 格式

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Weekly Report To Annual AI skill do?

从飞书邮箱读取周报邮件,根据年度报告模板生成年度总结报告

Why use Weekly Report To Annual on TypingMind?

Because you install it once and use it with any model. Weekly Report To Annual is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Weekly Report To Annual in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LingyiChen-AI/OpenSkills/tree/main/examples/weekly-report-to-annual. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Weekly Report To Annual?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Weekly Report To Annual?

As many as you like. As long as a model supports skills, you can use Weekly Report To Annual with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Weekly Report To Annual AI skill free?

Yes. It is published on GitHub by LingyiChen-AI under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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