Skills cover-letter-drafter
Generates professional cover letters for journal submissions and job
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 "$T/skills/aipoch-ai/cover-letter-drafter" ~/.claude/skills/clawdbot-skills-cover-letter-drafter && rm -rf "$T"
manifest:
skills/aipoch-ai/cover-letter-drafter/SKILL.mdsource content
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 |
|---|---|---|---|---|
| string | job | No | Cover letter type (journal, job, fellowship) |
| string | - | Yes | Target journal or institution |
| string | - | Yes | Comma-separated key points to highlight |
| string | - | No | Manuscript title (for journal submissions) |
| string | - | No | Significance statement (for journal submissions) |
| string | Applicant | No | Author or applicant name |
| string | - | No | Position title (for job applications) |
| string | - | No | Fellowship name (for fellowship applications) |
| 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
- 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