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Skills mnemonic-generator

Create memory aids for anatomy and pharmacology

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 "$T/skills/aipoch-ai/mnemonic-generator" ~/.claude/skills/openclaw-skills-mnemonic-generator && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/aipoch-ai/mnemonic-generator" ~/.openclaw/skills/openclaw-skills-mnemonic-generator && rm -rf "$T"
manifest: skills/aipoch-ai/mnemonic-generator/SKILL.md
tags
#mnemonic#education#memory#generation#medical
source content

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 IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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