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Mentorship Meeting Agenda

by @aipoch-ai

Generate structured agendas for mentor-student one-on-one meetings

Versionv1.0.0
Downloads375
TERMINAL
clawhub install mentorship-meeting-agenda

πŸ“– About This Skill


name: mentorship-meeting-agenda description: Generate structured agendas for mentor-student one-on-one meetings version: 1.0.0 category: Career tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

Mentorship Meeting Agenda

Generate structured agendas for mentor-student one-on-one meetings to ensure productive discussions.

Usage

python scripts/main.py --student "Alice" --phase early --output agenda.md

Parameters

  • --student: Student name
  • --phase: Career phase (early/mid/late)
  • --topics: Specific topics to cover
  • --output: Output file
  • Agenda Sections

    1. Progress updates (5 min) 2. Current challenges (10 min) 3. Goal setting (10 min) 4. Resource needs (5 min) 5. Action items (5 min)

    Output

  • Structured meeting agenda
  • Time allocations
  • Discussion prompts
  • Follow-up tracker
  • 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 β†’ Expected output 2. Edge Case: Invalid input β†’ Graceful error handling 3. Performance: Large dataset β†’ Acceptable processing time

    Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:
  • - Performance optimization - Additional feature support

    πŸ’‘ Examples

    python scripts/main.py --student "Alice" --phase early --output agenda.md
    

    βš™οΈ Configuration

    No additional Python packages required.