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

# 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

{
  "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