— name: grant-budget-justification description: Generate narrative budget justifications for NIH/NSF applications version: 1.0.0 category: Grant tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' — # Grant Budget Justification Narrative budget explanations for grant proposals. ## Use Cases – Equipment purchases – Personnel costs – Supplies and reagents – Travel and dissemination ## Parameters | Parameter | Type | Default | Required | Description | |———–|——|———|———-|————-| | `–input`, `-i` | string | – | Yes | Path to budget items file (JSON/CSV) | | `–justification-type` | string | – | Yes | Type of justification (Equipment, Personnel, Other) | | `–agency` | string | NIH | No | Funding agency (NIH, NSF) | | `–output`, `-o` | string | stdout | No | Output file path | | `–format` | string | text | No | Output format (text, markdown, docx) | ## Returns – Narrative justification text – Cost-benefit rationale – Compliance with agency requirements ## Example Input: $50,000 for mass spectrometer Output: Justification emphasizing essentiality and cost-sharing ## 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