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Dei Statement Drafter

该 Skill 通过本地 Python 脚本，按 faculty、postdoc、grant 等模板，结合用户提供的 DEI 经历文件，生成结构化的多元化、公平与包容陈述，并给出章节建议与最佳实践提示，支持将结果写入指定输出文件。

aipoch-ai
aipoch-ai
热度 83.96
最近更新 2026-05-17 15:01:37
分类：创作依赖：代码执行 / 读写文件应用场景：文案创作 / 模板 / 研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add dei-statement-drafter
Metadata
name
dei-statement-drafter
description
Draft Diversity, Equity, and Inclusion statements for academic applications
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

DEI Statement Drafter

Draft Diversity, Equity, and Inclusion (DEI) statements for academic job applications and grant proposals.

Usage

python scripts/main.py --template faculty --experiences experiences.txt

Parameters

Parameter Type Default Required Description
--template, -t string faculty No Statement template (faculty, postdoc, grant)
--experiences, -e string - No File with DEI-related experiences
--output, -o string - No Output file path
--best-practices, -b flag - No Show DEI statement best practices

Statement Components

  • Personal background and perspective
  • DEI-related experiences
  • Future plans and commitment
  • Specific actions and initiatives

Output

  • Structured DEI statement
  • Section suggestions
  • Best practice tips

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