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#

ParameterTypeRequiredDefaultDescription--research-areastrYes-Research field to analyze (e.g., "cancer immunotherapy")--yearsintNo3Analysis time window in years--outputstrNostdoutOutput file path for results--formatstrNojsonOutput format: json, csv, or text--top-nintNo10Number 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 IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

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