安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：可疑

OpenClaw 评估

The skill appears to do what it claims (generate ELN Markdown templates) and has no external dependencies or credential requests, but it executes a local Python script that writes files and the documentation/checklist calls out validations that are not obviously enforced — so you should inspect or sandbox it before running with real data or sensitive paths.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Eln Template Creator」。简介：Generate standardized experiment templates for Electronic Laboratory Notebooks。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aipoch-ai/eln-template-creator/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: eln-template-creator
description: Generate standardized experiment templates for Electronic Laboratory
  Notebooks
version: 1.0.0
category: Data
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: Medium
skill_type: Tool/Script
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'
---

# ELN Template Creator

ID: 139

Generate standardized experiment record templates for Electronic Laboratory Notebooks (ELN).

## Description

This Skill is used to generate standardized experiment record templates that comply with laboratory specifications, supporting multiple experiment types and custom fields.

## Usage

```bash
# Generate molecular biology experiment template
python scripts/main.py --type molecular-biology --output experiment_template.md

# Generate chemistry synthesis experiment template
python scripts/main.py --type chemistry --output chemistry_template.md

# Generate cell culture experiment template
python scripts/main.py --type cell-culture --output cell_culture_template.md

# Generate general experiment template
python scripts/main.py --type general --output general_template.md

# Custom template parameters
python scripts/main.py --type general --title "Protein Purification Experiment" --researcher "Zhang San" --output protein_purification.md
```

## Parameters

| Parameter | Type | Default | Required | Description |
|-----------|------|---------|----------|-------------|
| `--type` | string | - | Yes | Experiment type (general, molecular-biology, chemistry, cell-culture, animal-study) |
| `--output`, `-o` | string | stdout | No | Output file path |
| `--title` | string | - | No | Experiment title |
| `--researcher` | string | - | No | Researcher name |
| `--date` | string | - | No | Experiment date (YYYY-MM-DD) |
| `--project` | string | - | No | Project name/number |

## Supported Experiment Types

1. **general** - General experiment template
2. **molecular-biology** - Molecular biology experiments (PCR, cloning, electrophoresis, etc.)
3. **chemistry** - Chemical synthesis experiments
4. **cell-culture** - Cell culture experiments
5. **animal-study** - Animal experiments

## Output Format

Generated templates are in Markdown format, containing the following standard sections:

- Basic experiment information
- Experiment purpose
- Experiment materials and reagents
- Experiment equipment
- Experiment procedures
- Results recording
- Data analysis
- Conclusions and discussion
- Attachments and raw data

## Requirements

- Python 3.8+

## Author

OpenClaw

## Risk Assessment

| Risk Indicator | Assessment | Level |
|----------------|------------|-------|
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |

## Security Checklist

- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] Input file paths validated (no ../ traversal)
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no stack traces exposed)
- [ ] Dependencies audited
## Prerequisites

No additional Python packages required.

## Evaluation Criteria

### Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable

### Test Cases
1. **Basic Functionality**: Standard input → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time

## Lifecycle Status

- **Current Stage**: Draft
- **Next Review Date**: 2026-03-06
- **Known Issues**: None
- **Planned Improvements**: 
  - Performance optimization
  - Additional feature support