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

# 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

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

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