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mentorship-meeting-agenda

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

61

3.12x
Quality

41%

Does it follow best practices?

Impact

100%

3.12x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/Academic Writing/mentorship-meeting-agenda/SKILL.md
SKILL.md
Quality
Evals
Security

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 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
Repository
aipoch/medical-research-skills
Last updated
Created

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