Skills sop-writer

Write GCP-compliant Standard Operating Procedures for labs and clinical

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/sop-writer-aipoch" ~/.claude/skills/clawdbot-skills-sop-writer && rm -rf "$T"

manifest: skills/aipoch-ai/sop-writer-aipoch/SKILL.md

source content

SOP Writer

Generate compliant Standard Operating Procedures.

Use Cases

Sample processing SOPs
Equipment calibration SOPs
Safety procedure documentation
GCP compliance procedures

Parameters

```
procedure_name
```
: SOP title
```
scope
```
: Applicable areas
```
responsibility
```
: Who performs

Returns

ISO 15189 compliant SOP
Version control section
Signature page template

Example

Input: Blood sample processing Output: Complete SOP with materials, methods, QC checks

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