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// Skill profile

Mnemonic Generator

name: mnemonic-generator

by aipoch-ai · published 2026-04-01

数据处理API集成
Total installs
0
Stars
★ 0
Last updated
2026-04
// Install command
$ claw add gh:aipoch-ai/aipoch-ai-mnemonic-generator
View on GitHub
// Full documentation

---

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

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