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SOP Writer

按流程名称、适用范围、责任人等参数生成临床实验室标准操作规程,输出含版本控制与签名页模板;材料称脚本在本地执行且无外部网络调用,输入读取与输出写入工作区文件。

aipoch-ai
aipoch-ai
热度 39.86
最近更新 2026-05-11 17:38:37
分类:效率依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:法律文档 / 模板 / 工作流

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add sop-writer-aipoch
Metadata
name
sop-writer
description
Write GCP-compliant Standard Operating Procedures for labs and clinical sites
version
1.0.0
category
Pharma
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

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

  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