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Install in OpenClaw
/install eln-template-creator
Description
Generate standardized experiment templates for Electronic Laboratory Notebooks
README (SKILL.md)
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
| 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
- general - General experiment template
- molecular-biology - Molecular biology experiments (PCR, cloning, electrophoresis, etc.)
- chemistry - Chemical synthesis experiments
- cell-culture - Cell culture experiments
- 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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
Usage Guidance
This skill appears coherent for producing ELN templates, but it runs a local Python script that writes files. Before running it on sensitive systems or with important file paths: 1) review the full scripts/main.py source to confirm there are no network calls, shell.exec/subprocess calls, or code that reads unrelated filesystem locations; 2) verify how the script handles the --output path (ensure it prevents directory traversal and overwriting important files); 3) run it in a sandboxed environment or container with a restricted working directory and non-privileged user; 4) prefer supplying explicit safe output paths (not user home or system directories); and 5) if you lack the ability to review the code, avoid running it with production data or secrets. If you want, I can scan the remainder of scripts/main.py for dangerous patterns (networking, subprocess, file traversal) — provide the full file text and I will check.
Capability Analysis
Type: OpenClaw Skill
Name: eln-template-creator
Version: 0.1.0
The skill bundle is a legitimate utility designed to generate standardized Markdown templates for Electronic Laboratory Notebooks (ELN). The core logic in `scripts/main.py` uses standard Python libraries to format user-provided metadata into predefined experiment templates and write them to a specified output file. No evidence of data exfiltration, network communication, obfuscation, or malicious intent was found. While the script lacks path sanitization for the output file, the documentation in `SKILL.md` transparently identifies this as a potential risk area, and the overall behavior is strictly aligned with the stated purpose.
Capability Assessment
Purpose & Capability
The name, description, SKILL.md usage examples, and the included Python script align: the tool generates Markdown experiment templates for ELNs and supports multiple experiment types. There are no unrelated requested credentials, binaries, or install actions.
Instruction Scope
Runtime instructions simply run scripts/main.py with CLI options and write an output file. That scope matches the stated purpose. However the SKILL.md includes a security checklist (e.g., "Input file paths validated (no ../ traversal)") but the documentation does not show that these checks are implemented. Because the script will write files specified by the user, lack of explicit path validation could allow accidental or malicious overwriting of files outside the workspace if not handled properly.
Install Mechanism
No install spec is present (instruction-only with an included script). This is low-risk compared with remote downloads; nothing will be automatically fetched from the network during install.
Credentials
The skill requires no environment variables, credentials, or config paths. That is proportionate for a local template generator.
Persistence & Privilege
The skill is not marked always:true and does not request permanent presence or modify other skills. It can be invoked by the agent, which is the platform default and appropriate here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install eln-template-creator - After installation, invoke the skill by name or use
/eln-template-creator - Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of eln-template-creator.
- Generates standardized experiment record templates for Electronic Laboratory Notebooks (ELN).
- Supports multiple experiment types: general, molecular-biology, chemistry, cell-culture, animal-study.
- Allows customization with parameters such as title, researcher, date, and project.
- Outputs templates in Markdown format with structured experiment sections.
- No additional Python packages required; compatible with Python 3.8+.
Metadata
Frequently Asked Questions
What is Eln Template Creator?
Generate standardized experiment templates for Electronic Laboratory Notebooks. It is an AI Agent Skill for Claude Code / OpenClaw, with 228 downloads so far.
How do I install Eln Template Creator?
Run "/install eln-template-creator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Eln Template Creator free?
Yes, Eln Template Creator is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Eln Template Creator support?
Eln Template Creator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Eln Template Creator?
It is built and maintained by AIpoch (@aipoch-ai); the current version is v0.1.0.
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