grant-budget-justification

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