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在 OpenClaw 中安装
/install grant-funding-scout
功能描述
NIH funding trend analysis to identify high-priority research areas
使用说明 (SKILL.md)
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
安全使用建议
This is a demo tool using embedded mock data and appears coherent and low-risk for local use. Before using it in production: (1) review and implement safe integration with real APIs (NIH RePORTER, NSF) and only supply necessary API keys; (2) confirm any output file path is intended (the script will write JSON when --output is used); (3) if you modify the script to fetch remote data, perform a security review for network calls and credential handling; (4) run the script in a sandbox or isolated environment if you are unsure about changes. The only minor inconsistency is the SKILL.md mentioning reading input files while the current code does not — verify intended input behavior if you expect file inputs.
功能分析
Type: OpenClaw Skill
Name: grant-funding-scout
Version: 0.1.0
The skill bundle is a harmless demonstration tool for analyzing mock NIH funding trends. The Python script (scripts/main.py) uses hardcoded data and standard libraries to generate reports, with no network activity, sensitive data access, or suspicious execution patterns.
能力评估
Purpose & Capability
Name, description, and provided code align: a local/demo funding trend analyzer using embedded mock data. Required binaries/env/configs are none, which is proportionate for the stated demonstration purpose.
Instruction Scope
SKILL.md describes a demo using mock data and notes production integration would require external APIs; runtime instructions and the Python script operate locally and do not call external services. The SKILL.md's risk table mentions 'Read input files' but the shipped script only writes an output file when --output is supplied and does not read arbitrary input files — minor mismatch between docs and code.
Install Mechanism
No install spec is provided (instruction-only + one script). No downloads or external packages are required; lowest-risk install posture.
Credentials
No environment variables, secrets, or credentials are required by the skill. That matches the demo nature and is proportionate.
Persistence & Privilege
Skill is not forced-always, does not request persistent privileges, and does not modify other skills or system-wide settings. It runs as a standalone script when invoked.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install grant-funding-scout - 安装完成后，直接呼叫该 Skill 的名称或使用
/grant-funding-scout触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
- Initial release of grant-funding-scout skill (v1.0.0, demonstration version using mock data)
- Analyzes NIH funding trends to identify high-priority research areas
- Provides analysis of top-funded institutions, emerging topics, and funding trends by research area and time window
- Outputs results in JSON, CSV, or text formats
- Includes detailed risk assessment and security checklist
- For educational/demo use only—future production versions intended to integrate with real funding databases
元数据
常见问题
Grant Funding Scout 是什么？
NIH funding trend analysis to identify high-priority research areas. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 175 次。
如何安装 Grant Funding Scout？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install grant-funding-scout」即可一键安装，无需额外配置。
Grant Funding Scout 是免费的吗？
是的，Grant Funding Scout 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Grant Funding Scout 支持哪些平台？
Grant Funding Scout 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Grant Funding Scout？
由 AIpoch（@aipoch-ai）开发并维护，当前版本 v0.1.0。
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