Agent skill

Experiment Log Standardizer

by Yuan1z0825 in Yuan1z0825/nature-skills

Turns uploaded photos, voice notes, text or local files into standardized experiment logs in Markdown with YAML frontmatter, optionally through Feishu CLI and Obsidian.

MITAuto-check passedResearch & Science

SKILL.md written in Chinese; this summary is our English description.

Install Experiment Log Standardizer

skills CLI
$ npx skills add Yuan1z0825/nature-skills --skill nature-experiment-log -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yuan1z0825/nature-skills nature-experiment-log --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yuan1z0825/nature-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-experiment-log .claude/skills/nature-experiment-log && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
nature-experiment-log
GitHub stars
47k
Used in
2 other repos
Token cost
~942 tokens
SKILL.md length
222 words
Files
11 (incl. references)
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Turns uploaded photos, voice notes, text or local files into standardized experiment logs in Markdown with YAML frontmatter, optionally through Feishu CLI and Obsidian.

  • Works in 8 steps: 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。 → 通过 vision_analyze 和文本解析提取结构化信息。 → 对缺失或模糊字段向用户确认,不猜测实验条件或结果。 → …
  • Logging lab experiments from photos, voice notes or typed notes
  • SKILL.md covers 输入方式, 输出方式, 处理流程 and 目录结构, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Input can be uploaded images, audio, transcripts or text, local files or folders, or messages and attachments from a Feishu group through an optional feishu-cli-integration skill. Output goes to a local Markdown folder you name, with the Markdown returned for you to save if no folder is given, or into an Obsidian vault through the optional obsidian skill, using bundled templates for an experiment index, an anomaly log and equipment tracking.

Processing runs in steps: collect the material, extract structured information with vision analysis and text parsing, ask about missing or ambiguous fields instead of guessing conditions or results, confirm the output method and folder, and generate an experiment ID and a sample batch ID. The agent then writes the log file, archives the raw attachments in a dated folder with links back, updates the index, adds an anomaly record when needed, and tells you where everything is.

Experiment IDs combine a system code, an equipment code, the date and a daily sequence number, and the equipment codes cover furnaces, an electrochemical workstation and a glove box, extendable to your own equipment. Three worked examples cover corrosion immersion, electrochemical characterization and thermal stability. Feishu input needs a bot with message and resource permissions; the core flow needs neither Feishu nor Obsidian. The skill text is written in Chinese.

When your agent uses it

  • Logging lab experiments from photos, voice notes or typed notes
  • Archiving raw experiment material next to a structured log
  • Tracking equipment use and anomalies in an Obsidian vault

Example prompts

  • “Turn these photos and my voice transcript from today's furnace run into an experiment log.”
  • “Read the files in ./raw/thermal and write a standardized log, and ask me about anything unclear.”
  • “Log the CV test notes from ./lab-notes into Markdown and save it in my vault.”

Requirements

  • Optionally the obsidian skill and a vault
  • Optionally feishu-cli-integration, with a bot that has message and resource permissions

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  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。

What it can do on your machine

Read from SKILL.md and the folder at commit e605b35. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • obsidian.md
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Experiment Log Standardizer loads about 942 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 26 tokens; SKILL.md has 222 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~942
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Yuan1z0825/nature-skills at commit e605b35, republished under its MIT licence (© Yuan1z0825). 222 words, ~942 tokens.

Download SKILL.mdSave it as .claude/skills/nature-experiment-log/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
nature-experiment-log
description
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。
license
MIT
metadata.author
Jiahao8595

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:message、im:message.group_msg 和 im: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 cd 到 raw/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

© Yuan1z0825, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in skills/nature-experiment-log of Yuan1z0825/nature-skills.

  • SKILL.md
  • README.md
  • README_EN.md
  • agents/openai.yaml
  • manifest.yaml
  • references/example-electrochemical.md
  • references/example-log.md
  • references/example-thermal-stability.md
  • templates/anomaly-log.md
  • templates/equipment-tracking.md
  • templates/experiment-index.md

Open the folder on GitHubat commit e605b35

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Yuan1z0825/nature-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Experiment Log Standardizer compared with similar skills
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Experiment Log Standardizer this skillYuan1z0825/nature-skills47k2 repos~942Automated safety check: PassMIT
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Obsidian MarkdownAtmosphere/atmosphere3.8k20 repos~1.3kAutomated safety check: PassApache-2.0
Obsidian MarkdownKevRojo/Dulus167—~1.4kAutomated safety check: PassGPL-3.0
Obsidian Paper VaultAperivue/medsci-skills333—~1.6kAutomated safety check: PassMIT
Luhmann Note Numberertwhsi/skills259—~1.5kAutomated safety check: PassNone

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Questions about Experiment Log Standardizer

What does Experiment Log Standardizer do?

Turns uploaded photos, voice notes, text or local files into standardized experiment logs in Markdown with YAML frontmatter, optionally through Feishu CLI and Obsidian. Input can be uploaded images, audio, transcripts or text, local files or folders, or messages and attachments from a Feishu group through an optional feishu-cli-integration skill. Output goes to a local Markdown folder you name, with the Markdown returned for you to save if no folder is given, or into an Obsidian vault through the optional obsidian skill, using bundled templates for an experiment index, an anomaly log and equipment tracking.

When should I use Experiment Log Standardizer?

Experiment Log Standardizer fits situations like: logging lab experiments from photos, voice notes or typed notes; archiving raw experiment material next to a structured log; tracking equipment use and anomalies in an Obsidian vault.

How do I install Experiment Log Standardizer in Claude Code?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-experiment-log -a claude-code`. Or copy the skill folder (skills/nature-experiment-log in Yuan1z0825/nature-skills) into .claude/skills/nature-experiment-log in your project. Claude Code loads it when a task matches its description.

How do I install Experiment Log Standardizer in Codex?

Run `npx skills add Yuan1z0825/nature-skills --skill nature-experiment-log -a codex`. Or copy the skill folder (skills/nature-experiment-log in Yuan1z0825/nature-skills) into .agents/skills/nature-experiment-log in your project. Codex loads it when a task matches its description.

Can I use Experiment Log Standardizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Yuan1z0825/nature-skills --skill nature-experiment-log -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-experiment-log, .gemini/skills/nature-experiment-log, .github/skills/nature-experiment-log and .opencode/skills/nature-experiment-log in your project.

What does Experiment Log Standardizer need to run?

SKILL.md names no scripts, command-line tools or credentials: Experiment Log Standardizer is instructions for the agent only. Our summary lists: Optionally the obsidian skill and a vault; Optionally feishu-cli-integration, with a bot that has message and resource permissions.

Does Experiment Log Standardizer access the network?

SKILL.md names 2 domains. As links in the text: obsidian.md and github.com. This is read from the text; nothing was executed.

Is Experiment Log Standardizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Experiment Log Standardizer use?

Experiment Log Standardizer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Experiment Log Standardizer use?

About 942 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Experiment Log Standardizer?

Skills that share tags, products or a category with Experiment Log Standardizer: Knap Markdown Templates (kepano/obsidian-skills, 49k stars), Obsidian Markdown (Atmosphere/atmosphere, 3.8k stars), Obsidian Markdown (KevRojo/Dulus, 167 stars) and Obsidian Paper Vault (Aperivue/medsci-skills, 333 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Experiment Log Standardizer?

Yuan1z0825 (a GitHub user) maintains it in Yuan1z0825/nature-skills, which has 47,222 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 11, 2026.

Source: Yuan1z0825/nature-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.