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Mnemonic Generator

面向医学学习的记忆辅助工具,接收主题、待记忆条目和风格(首字母缩略/故事/视觉),生成记忆口诀、关联解释、备选方案与使用提示。附带 Python 脚本在本地运行,不使用外部 API,可读写工作区文件。

aipoch-ai
aipoch-ai
热度 42.02
最近更新 2026-05-11 17:37:12
分类:知识依赖:代码执行 / 读写文件应用场景:教学辅导 / 概念解释

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 mnemonic-generator
Metadata
name
mnemonic-generator
description
Create memory aids for anatomy and pharmacology
version
1.0.0
category
Education
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Mnemonic Generator

Medical memory aid creator.

Use Cases

  • Cranial nerve memorization
  • Drug side effects
  • Anatomy structures
  • Biochemistry pathways

Parameters

  • topic: Subject matter
  • items_to_remember: List
  • style: Acronym/story/visual

Returns

  • Custom mnemonics
  • Explanation of connection
  • Alternative suggestions
  • Usage tips

Example

Cranial nerves: "On Old Olympus Towering Tops..."

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