Skills mentorship-meeting-agenda
Generate structured agendas for mentor-student one-on-one meetings
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 "$T/skills/aipoch-ai/mentorship-meeting-agenda" ~/.claude/skills/openclaw-skills-mentorship-meeting-agenda && rm -rf "$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 "$T/skills/aipoch-ai/mentorship-meeting-agenda" ~/.openclaw/skills/openclaw-skills-mentorship-meeting-agenda && rm -rf "$T"
manifest:
skills/aipoch-ai/mentorship-meeting-agenda/SKILL.mdsource content
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 name--student
: Career phase (early/mid/late)--phase
: Specific topics to cover--topics
: Output file--output
Agenda Sections
- Progress updates (5 min)
- Current challenges (10 min)
- Goal setting (10 min)
- Resource needs (5 min)
- 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
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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