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// Skill profile

Mentorship Meeting Agenda

name: mentorship-meeting-agenda

by aipoch-ai · published 2026-04-01

数据处理API集成
Total installs
0
Stars
★ 0
Last updated
2026-04
// Install command
$ claw add gh:aipoch-ai/aipoch-ai-mentorship-meeting-agenda
View on GitHub
// Full documentation

---

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 → 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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