mentorship-meeting-agenda
Generate structured agendas for mentor-student one-on-one meetings
Best use case
mentorship-meeting-agenda is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate structured agendas for mentor-student one-on-one meetings
Teams using mentorship-meeting-agenda should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mentorship-meeting-agenda/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mentorship-meeting-agenda Compares
| Feature / Agent | mentorship-meeting-agenda | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate structured agendas for mentor-student one-on-one meetings
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Mentorship Meeting Agenda Generate structured agendas for mentor-student one-on-one meetings to ensure productive discussions. ## Usage ```bash 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 → 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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