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Lab Report Writer

Community
mingchen666
lab-report-writer

Use for writing, editing, polishing, or structuring lab reports and course experiment reports across disciplines. Handles cases with a provided template, raw data, images, notes, or only a brief requirement, and can produce DOCX or Markdown deliverables.

Overview

Publishermingchen666
RepositoryReviva
Skill namelab-report-writer
Stars
293
Forks
31
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by mingchen666 on GitHub. Read the source before you install it.

Installation

Install the Lab Report Writer 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.
    https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/lab-report-writer
  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/mingchen666/Reviva.git /tmp/Reviva
mkdir -p .claude/skills
cp -r /tmp/Reviva/electron/builtin-assets/skills/lab-report-writer .claude/skills/lab-report-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lab Report Writer 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 Lab Report Writer 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 Lab Report Writer 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.

实验报告写作

目标

帮助用户完成可提交的实验报告,而不是只生成泛泛的说明文字。默认面向高校课程、专业课实验、课程设计小实验和训练性实验,覆盖物理、化学、生物、材料、电子电路、自动化、计算机、机械、心理学、环境、医学基础等常见方向。

输入类型

用户可能只提供其中一部分:

  • 实验名称、课程名、专业方向、教师要求。
  • 实验指导书、讲义、评分标准、知识库资料。
  • Word 模板、历史报告、待补全文档。
  • 原始数据、表格、截图、实验照片、仪器输出文件。
  • 只说一句“帮我写某某实验报告”。

不要因为缺少全部材料就停止。能合理生成框架时先生成;不能确定的数据、图片、个人操作记录、教师指定格式必须标为“待补充”,不要编造。

总流程

  1. 识别实验类型、交付格式、是否有模板、是否有原始数据。
  2. 有模板时,先用 document_read 读取模板结构,再用 office_write(operation="edit") 生成编辑副本,尽量保留模板样式、页眉页脚、编号和固定字段。
  3. 无模板时,默认生成结构完整的 DOCX;如果用户明确要 Markdown,再输出 .md。
  4. 有资料时优先依据资料、用户文件和知识库;没有资料时可按该专业常见实验报告规范生成通用框架,并明确待确认项。
  5. 有数据时必须进行基础整理:单位、有效数字、表格列名、计算公式、结果与结论是否对应。
  6. 结束前执行质量自检,并在回复中说明输出文件路径和需要用户补充的内容。

默认报告结构

无模板时使用以下结构,并按实验类型调整:

  1. 封面信息:课程名称、实验名称、姓名、学号、班级、日期、指导教师。未知项写“待补充”。
  2. 摘要:100-200 字,说明目的、方法、关键结果和结论。
  3. 实验目的:3-5 条,使用可验证动词。
  4. 实验原理:核心概念、公式、模型、反应机理、系统框图或算法思想。
  5. 仪器、材料与环境:设备型号、试剂浓度、软件版本、实验平台、关键参数。
  6. 实验步骤:按实际操作顺序写,避免空泛。
  7. 数据记录与处理:原始数据表、计算过程、图表、误差或不确定度。
  8. 结果与分析:解释数据趋势、异常值、误差来源、与理论的偏差。
  9. 思考题或讨论:如果指导书有问题,逐题回答;没有则提供专业相关讨论。
  10. 结论:只总结由数据支持的结论,不扩大。
  11. 参考文献:教材、指导书、标准、论文或网页,缺少来源时标注待补充。
  12. 附录:原始记录、代码、完整计算、图片说明。

专业适配规则

  • 物理/电子:突出实验原理、测量方法、误差分析、不确定度、仪器精度、拟合结果。
  • 化学/材料:突出试剂与安全、反应条件、现象记录、产率/浓度计算、谱图或显微图分析。
  • 生物/医学基础:突出样本来源、变量控制、重复次数、统计方法、生物安全和伦理边界。
  • 计算机/自动化:突出需求、算法/控制策略、实验环境、输入输出、测试用例、性能指标和结果截图。
  • 机械/工程训练:突出设备参数、工艺流程、结构图、测量数据、失效/误差原因和改进建议。
  • 心理/教育实验:突出研究假设、被试、材料、程序、统计检验、伦理说明和限制。

DOCX 生成要求

使用 office_write 时:

  • 不确定元素、属性、路径时,先调用 office_write(operation="help", format="docx", element=...)。
  • 大量段落、表格或样式设置可使用 useBatch=true。
  • 只有 typed actions 不能表达时才使用 raw_set,必须设置 allowRawXml=true 且每个 raw action confirm=true。
  • 编辑已有模板时绝不覆盖源文件;必须输出副本。
  • 文档层级使用标题样式,正文统一字体和行距。
  • 表格必须有表名、列名、单位;图必须有图题和说明。
  • 如果图片路径不可用,插入清晰的图片占位说明,不伪造图片。
  • 公式可以先用可读文本表达;复杂公式需要保真时可说明用户后续在 Word 中替换为公式对象。

数据和事实规则

  • 不编造实验数据、仪器型号、姓名学号、教师姓名、真实参考文献页码。
  • 对用户给出的数据做合理计算,但必须保留计算依据。
  • 结论必须来自数据或资料;没有数据时写预期结论或待补充结论,并标明不确定。
  • 可能影响评分的要求优先服从用户模板、评分标准和教师说明。

质量自检

交付前至少检查:

  • 章节是否完整,是否匹配模板要求。
  • 是否还有“待补充”项,并在最终回复中列出。
  • 数据单位、有效数字和公式是否前后一致。
  • 图表是否有编号、标题和引用。
  • 结果分析是否解释异常值、误差来源和改进方向。
  • 文件是否写入当前 Agent 输出目录。

输出方式

默认优先输出 .docx。如果 OfficeCLI 不可用或用户只要草稿,可输出 Markdown 草稿,并说明未生成 DOCX 的原因。

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 Lab Report Writer AI skill do?

Use for writing, editing, polishing, or structuring lab reports and course experiment reports across disciplines. Handles cases with a provided template, raw data, images, notes, or only a brief requirement, and can produce DOCX or Markdown deliverables.

Why use Lab Report Writer on TypingMind?

Because you install it once and use it with any model. Lab Report Writer 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 Lab Report Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/lab-report-writer. 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 Lab Report Writer?

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 Lab Report Writer?

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

Is the Lab Report Writer AI skill free?

It is published on GitHub by mingchen666. Check the repository for licensing terms. 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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