Grant Funding Scout
name: grant-funding-scout
by aipoch-ai · published 2026-04-01
$ claw add gh:aipoch-ai/aipoch-ai-grant-funding-scout---
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
**⚠️ 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 dominate
Data 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 → Expected output
2. **Edge Case**: Invalid input → Graceful error handling
3. **Performance**: Large dataset → Acceptable processing time
Lifecycle Status
- Performance optimization
- Additional feature support
More tools from the same signal band
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
Sign plugins, rotate agent credentials without losing identity, and publicly attest to plugin behavior with verifiable claims and authenticated transfers.
The philosophical layer for AI agents. Maps behavior to Spinoza's 48 affects, calculates persistence scores, and generates geometric self-reports. Give your...