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
- 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