Grant Funding Scout
by @aipoch-ai
NIH funding trend analysis to identify high-priority research areas
clawhub install grant-funding-scoutπ About This Skill
name: grant-funding-scout description: NIH funding trend analysis to identify high-priority research areas version: 1.0.0 category: Research tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'
Grant Funding Scout
β οΈ 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
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
Example
Input: "cancer immunotherapy", years=3 Output: Funding increased 40% YoY; CAR-T and checkpoint inhibitors dominateData Sources
Current Version: Uses mock funding data for demonstration purposes.For Production Use:
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
Prerequisites
No additional Python packages required.
Evaluation Criteria
Success Metrics
Test Cases
1. Basic Functionality: Standard input β Expected output 2. Edge Case: Invalid input β Graceful error handling 3. Performance: Large dataset β Acceptable processing timeLifecycle Status
β‘ When to Use
π‘ Examples
Input: "cancer immunotherapy", years=3 Output: Funding increased 40% YoY; CAR-T and checkpoint inhibitors dominate
βοΈ Configuration
No additional Python packages required.