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 hamzabellouch/agent-skills --skill academic-nature-nature-experiment-loggit clone --depth 1 https://github.com/hamzabellouch/agent-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/hamzabellouch/agent-skills/academic-nature-nature-experiment-log)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/academic-nature-nature-experiment-log"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/academic-nature-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/hamzabellouch/agent-skills/academic-nature-nature-experiment-log"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/academic-nature-nature-experiment-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.01646 |
| Opus 5 | $0.00026 | $0.00823 |
| Sonnet 5 | $0.00010 | $0.00329 |
| Haiku 4.5 | $0.00005 | $0.00165 |
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 8d 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 — 155 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
触发条件
用户通过以下任一方式提交实验原始材料时自动加载：
- 路径 A — CLI 直接提交（图片 / 语音转录 / 文字）
- 路径 B — 发到飞书科研群，通过
feishu-cli-integration扫描
前置依赖
需要配合 feishu-cli-integration skill 使用。确保：
- 飞书 bot 已添加到目标群
- bot 权限：
im:message+im:resource+im:message.group_msg - 群 ID 配置在 skill 上下文中
处理流程
- 接收材料 → vision_analyze 读图 + 提取结构化信息
- 生成实验 ID + 样品批次 ID
- 写出标准日志到
wiki/实验日志/{体系}/{类型}/{exp_id}.md - 原始材料（图片等）归档到
raw/experiments/YYYY.MM.DD_描述_EXPID/ - 日志末尾加「原始材料」段落，引用 raw 路径
- 检查异常 → 有则追加
异常记录.md - 追加操作记录到日志索引
- 告知用户写入位置
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。
Obsidian 集成
本 skill 设计为与 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.md |
templates/experiment-index.md |
Dataview 查询仪表盘 |
实验日志/异常记录.md |
templates/anomaly-log.md |
异常记录 |
实验日志/公共/设备与试剂追踪.md |
templates/equipment-tracking.md |
设备与试剂追踪 |
What ships with it
9 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.
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.
- 8d ago First seen · 155 lines · 51 tokens per session scan A fd079f111119
nature-experiment-log is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 1,646 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
nsfc-budget
A tool that creates an editable LaTeX budget justification and renders it as a PDF for an NSFC research-funding application. NSFC is China’s National Natural Science Foundation, and a budget justification explains why proposed costs are needed.
nsfc-ref-alignment
A read-only checker for references in NSFC LaTeX proposals. It compares citations with the bibliography and flags missing entries, field errors, and possible mismatches between claims and papers.
nature-data
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data…
publication-chart-skill
This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants…
semantic-scholar-deep
Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation…
thesis-control
Use when AI-assisted thesis or manuscript edits risk claim drift, scope creep, loss of intended use, experiment-role promotion, or repeated revisions that fail to converge; provides author-intent control, lightweight or strict contracts, drift audits, revision escalation, and human gates.