Grant Funding Scout

⚠️ Note: This is a demonstration/illustrative version using mock data for educational purposes. For production use, integration with real funding databases (NIH RePORTER, NSF Award Search, etc.) is required.

Analyze funding patterns to guide research strategy.

Use Cases

• Identifying "hot" research topics
• Avoiding oversaturated areas
• Strategic grant positioning

Parameters

Parameter	Type	Required	Default	Description
--research-area	str	Yes	-	Research field to analyze (e.g., "cancer immunotherapy")
--years	int	No	3	Analysis time window in years
--output	str	No	stdout	Output file path for results
--format	str	No	json	Output format: json, csv, or text
--top-n	int	No	10	Number of top results to display

Returns

• Top-funded institutions and PIs
• Emerging topic identification
• Funding trend analysis

Example

Input: "cancer immunotherapy", years=3 Output: Funding increased 40% YoY; CAR-T and checkpoint inhibitors dominate

Data Sources

Current Version: Uses mock funding data for demonstration purposes.

For Production Use:

• NIH RePORTER API
• NSF Award Search API
• CORDIS (EU research)
• Federal RePORTER
• Private foundation databases

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