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
INLINECODE0	str	Yes	-	Research field to analyze (e.g., "cancer immunotherapy")
INLINECODE1

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. 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