Skill: aipoch-ai/cover-letter-drafter (version 0.1.1)

--- name: cover-letter-drafter description: Generates professional cover letters for journal submissions and job applications in medical and academic contexts. version: 1.0.0 category: Career tags: - cover-letter - job-application - journal-submission - academic-writing author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Cover Letter Drafter Creates tailored cover letters for academic and medical positions. ## Features - Journal submission cover letters - Job application cover letters - Fellowship application letters - Customizable templates ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--purpose` | string | job | No | Cover letter type (journal, job, fellowship) | | `--recipient`, `-r` | string | - | Yes | Target journal or institution | | `--key-points`, `-k` | string | - | Yes | Comma-separated key points to highlight | | `--title` | string | - | No | Manuscript title (for journal submissions) | | `--significance` | string | - | No | Significance statement (for journal submissions) | | `--author`, `--applicant`, `-a` | string | Applicant | No | Author or applicant name | | `--position` | string | - | No | Position title (for job applications) | | `--fellowship` | string | - | No | Fellowship name (for fellowship applications) | | `--output`, `-o` | string | - | No | Output JSON file path | ## Usage ```bash # Journal submission cover letter python scripts/main.py --purpose journal --recipient "Nature Medicine" \ --key-points "Novel findings,Clinical relevance" \ --title "Study X" --significance "major advance" --author "Dr. Smith" # Job application cover letter python scripts/main.py --purpose job --recipient "Harvard Medical School" \ --key-points "10 years experience,Published 20 papers" \ --position "Assistant Professor" --applicant "Dr. Jones" # Fellowship application python scripts/main.py --purpose fellowship --recipient "NIH" \ --key-points "Research excellence,Leadership skills" \ --fellowship "K99" --applicant "Dr. Lee" ``` ## Output Format ```json { "cover_letter": "string", "subject_line": "string", "word_count": "int" } ``` ## 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)