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Nature Experiment Log

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Yuan1z0825
nature-experiment-log

标准化实验日志记录——直接上传或读取本地图片、语音和文字,产出带 YAML frontmatter 的 Markdown;可选集成飞书 CLI 与 Obsidian。

Overview

PublisherYuan1z0825
Repositorynature-skills
Skill namenature-experiment-log
Stars
42.8K
Forks
2.3K
Bundled files
9
LicenseMIT
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.

  • 9 bundled files

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

  • Open source

    Published by Yuan1z0825 on GitHub. Read the source before you install it.

Installation

Install the Nature Experiment Log 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/Yuan1z0825/nature-skills.git /tmp/nature-skills
mkdir -p .claude/skills
cp -r /tmp/nature-skills/skills/nature-experiment-log .claude/skills/nature-experiment-log
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Experiment Log 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 Nature Experiment Log 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 Nature Experiment Log 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.

experiment-log — 实验日志标准化

输入方式

用户通过以下任一方式提交实验原始材料时自动加载:

  • 直接上传 — 在当前会话提交图片、音频、语音转录或文字。
  • 本地材料 — 提供本地文件或文件夹路径,由 agent 读取并整理。
  • 飞书群 — 通过可选的 feishu-cli-integration 读取群消息和附件。

输出方式

  • 本地 Markdown — 将日志和原始附件保存到用户指定的普通本地文件夹;未指定目录时,先返回可保存的 Markdown,不擅自选择路径。
  • Obsidian vault — 通过可选的 obsidian skill 写入 vault,并使用附带模板建立索引、异常记录和设备追踪。

核心流程不要求安装飞书或 Obsidian。使用飞书群输入时,才需要 bot 已加入目标群并具备 im:messageim:message.group_msgim:resource 权限。

处理流程

  1. 接收上传材料、读取本地文件,或从已配置的飞书群获取材料。
  2. 通过 vision_analyze 和文本解析提取结构化信息。
  3. 对缺失或模糊字段向用户确认,不猜测实验条件或结果。
  4. 确认输出方式和目标目录,生成实验 ID 与样品批次 ID。
  5. 写出 {OUTPUT_ROOT}/实验日志/{体系}/{类型}/{exp_id}.md
  6. 将原始附件归档到 {OUTPUT_ROOT}/raw/experiments/YYYY.MM.DD_描述_EXPID/,并在日志中建立引用。
  7. 如启用索引模板,更新实验索引;发现异常时追加异常记录。
  8. 告知用户生成文件及原始材料的具体位置。

模糊信息(温度记不清、样品编号不明)主动询问,不猜测写入。

目录结构

/vault/
├── raw/experiments/                       ← 原始层(归档)
│   └── YYYY.MM.DD_描述_EXPID/
│       ├── 笔记.md
│       ├── 图片/
│       └── 语音/
wiki/实验日志/                              ← 标准层(产出)
├── 实验索引.md
├── 异常记录.md
├── {体系A}/
│   ├── 实验类型1/
│   ├── 实验类型2/
│   └── ...
├── {体系B}/
│   └── ...
└── 公共/
    └── 设备与试剂追踪.md

实验 ID 规则

{体系代码}-{设备代码}-YYMMDD-{序号}
  │        │       │       └─ 当日序号(001 起)
  │        │       └─ 日期
  │        └─ 设备代码(M=马弗炉, T=管式炉, E=电化学, G=手套箱, F=可控气氛炉, B=通用)
  └─ 体系代码(自定义,如 CL / NO / OX / HY 等)

样品批次 ID 规则

{体系代码}-{候选编号}-B{序号}
  │        │         └─ 配盐批次序号
  │        └─ 候选配方编号
  └─ 体系代码

同一批样品跨多个实验时 sample_batch 保持一致,便于 dataview 追踪。

设备代码

代码设备场景
M马弗炉热处理、浸泡腐蚀
T管式炉气氛控制、脱水、热稳定性
E电化学工作站CV/SWV/EIS
G手套箱配盐、称量、取样
F可控气氛炉精密气氛控制
B通用干燥、清洗、制样

按实际设备扩展。

可选的 Obsidian 集成

本 skill 可以只向普通本地文件夹输出 Markdown,也可以与 Obsidian vault 配合使用。Obsidian 是一个基于本地 Markdown 文件的笔记系统,配合 Dataview 插件可实现实验数据的动态查询和仪表盘。

为什么用 Obsidian:

  • 所有日志为纯文本 Markdown,可版本控制、可全文搜索
  • YAML frontmatter 结构使 dataview 可自动生成实验列表、异常汇总、设备使用记录
  • 本地存储,无云依赖性,数据安全

安装 skill 后需在 vault 中创建以下文件:

文件模板用途
实验日志/实验索引.mdtemplates/experiment-index.mdDataview 查询仪表盘
实验日志/异常记录.mdtemplates/anomaly-log.md异常记录
实验日志/公共/设备与试剂追踪.mdtemplates/equipment-tracking.md设备与试剂追踪

将模板文件复制到你的 Obsidian vault 对应位置即可使用。

参考示例

references/ 目录包含三个完整的实验日志示例,覆盖常见实验类型:

文件实验类型
references/example-log.md材料腐蚀浸泡实验
references/example-electrochemical.md电化学表征(CV 窗口测试)
references/example-thermal-stability.md热稳定性实验

每个示例均包含完整的 YAML frontmatter 和 Markdown 正文,可直接作为模板修改使用。

可选的飞书 CLI 集成

需要从飞书群获取材料时,使用 feishu-cli-integration skill:

  • 拉消息:lark-cli im +chat-messages-list --chat-id oc_*** --page-size 30 --sort asc
  • 下载图片:lark-cli im +messages-resources-download --message-id *** --file-key *** --type image --output <相对路径>
  • ⚠️ --output 只接受相对路径,先 cdraw/experiments/ 归档目录

群 ID 和 bot 权限按 feishu-cli-integration skill 的配置获取。

自定义指南

  • 体系代码:按你的实验体系自定义(如 CL/NO/OR/PO)
  • 实验类型:在 wiki/实验日志/{体系}/ 下按需创建子目录
  • YAML 字段:模板是建议结构,可增删字段
  • 设备代码:按实际实验室设备扩展
  • 输出根目录:可以是普通本地文件夹,也可以是 Obsidian vault 根目录

相关文件

文件用途
references/example-log.md完整实验日志示例
wiki/实验日志/实验索引.mdDataview 仪表盘
wiki/实验日志/异常记录.md异常记录格式
wiki/实验日志/公共/设备与试剂追踪.md设备、试剂追踪

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 Nature Experiment Log AI skill do?

标准化实验日志记录——直接上传或读取本地图片、语音和文字,产出带 YAML frontmatter 的 Markdown;可选集成飞书 CLI 与 Obsidian。

Why use Nature Experiment Log on TypingMind?

Because you install it once and use it with any model. Nature Experiment Log 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 Nature Experiment Log in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-experiment-log. 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 Nature Experiment Log?

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 Nature Experiment Log?

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

Is the Nature Experiment Log AI skill free?

Yes. It is published on GitHub by Yuan1z0825 under the MIT 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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