Nature Skills is a collection of reusable skills that help AI agents handle academic writing and scientific visualization. Researchers and AI-assisted scholars use it to turn research tasks into repeatable workflows and usable outputs. The catalogue entries are skills from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Yuan1z0825/nature-skills --skill nature-experiment-loggit clone --depth 1 https://github.com/Yuan1z0825/nature-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-experiment-log)<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-experiment-log"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-experiment-log/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yuan1z0825/nature-skills/nature-experiment-log"><img src="https://agentmods.dev/badge/skills/yuan1z0825/nature-skills/nature-experiment-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.01804 |
| Opus 5 | $0.00023 | $0.00902 |
| Sonnet 5 | $0.00009 | $0.00361 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
nature-experiment-log scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
obsidianskill 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
- 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。
- 确认输出方式和目标目录,生成实验 ID 与样品批次 ID。
- 写出
{OUTPUT_ROOT}/实验日志/{体系}/{类型}/{exp_id}.md。 - 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。 - 如启用索引模板,更新实验索引;发现异常时追加异常记录。
- 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
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 238 B
- manifest.yaml 1.5 KB
- README_EN.md 2.3 KB
- README.md 2.2 KB
- references/example-electrochemical.md 1.8 KB
- references/example-log.md 2.0 KB
- references/example-thermal-stability.md 1.7 KB
- templates/anomaly-log.md 577 B
- templates/equipment-tracking.md 870 B
- templates/experiment-index.md 653 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 156 lines · 45 tokens per session scan A 1f7fffcbc950
nature-experiment-log is a skill published in the GitHub repository Yuan1z0825/nature-skills (40,913 stars, last pushed 2d ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,804 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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