Skill: Funding Trend Forecaster

---

name: funding-trend-forecaster

description: Predict funding trend shifts using NLP analysis of grant abstracts from

NIH, NSF, and Horizon Europe

version: 1.0.0

category: Grant

tags: []

author: AIPOCH

license: MIT

status: Draft

risk_level: High

skill_type: Hybrid (Tool/Script + Network/API)

owner: AIPOCH

reviewer: ''

last_updated: '2026-02-06'

---

# Skill: Funding Trend Forecaster

**ID:** 200

**Version:** 1.0.0

**Author:** OpenClaw Agent

**License:** MIT

---

Overview

Funding Trend Forecaster is an intelligent analysis tool that uses Natural Language Processing (NLP) technology to analyze awarded project abstracts from major global research funding agencies (NIH, NSF, Horizon Europe) and predict funding preference shift trends for the next 3-5 years.

Features

Installation

# Enter skill directory
cd skills/funding-trend-forecaster

# Install dependencies
pip install -r requirements.txt

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"

Dependencies

requests>=2.28.0
beautifulsoup4>=4.11.0
pandas>=1.5.0
numpy>=1.23.0
scikit-learn>=1.1.0
textblob>=0.17.1
nltk>=3.7
matplotlib>=3.6.0
seaborn>=0.12.0
wordcloud>=1.8.0
python-dateutil>=2.8.0

Usage

Command Line Interface

# Run full analysis workflow
python scripts/main.py --analyze-all --output report.json

# Analyze specific agency only
python scripts/main.py --source nih --months 6

# Generate visualization report
python scripts/main.py --visualize --input data.json --output charts/

# View trend forecast
python scripts/main.py --forecast --years 5 --output forecast.json

API Call

from scripts.main import FundingTrendForecaster

# Initialize forecaster
forecaster = FundingTrendForecaster()

# Collect data
forecaster.collect_data(sources=['nih', 'nsf', 'horizon_europe'], months=6)

# Execute analysis
results = forecaster.analyze_trends()

# Generate forecast
forecast = forecaster.predict_trends(years=5)

# Export report
forecaster.export_report(output_path='report.pdf', format='pdf')

Parameters

| Parameter | Type | Default | Required | Description |

|-----------|------|---------|----------|-------------|

| `--analyze-all` | flag | false | No | Run full analysis workflow on all sources |

| `--source` | string | - | No | Specific agency to analyze (nih, nsf, horizon_europe) |

| `--months` | int | 6 | No | Number of months of historical data to analyze |

| `--years` | int | 5 | No | Years ahead for trend prediction |

| `--visualize` | flag | false | No | Generate visualization charts |

| `--forecast` | flag | false | No | Generate trend forecast |

| `--input`, `-i` | string | - | No | Input data file path (for visualization/forecast) |

| `--output`, `-o` | string | - | No | Output file path |

| `--config` | string | config.json | No | Path to configuration file |

Data Sources

| Agency | Data Source URL | Update Frequency |

|------|-----------|---------|

| NIH | https://reporter.nih.gov/ | Daily |

| NSF | https://www.nsf.gov/awardsearch/ | Daily |

| Horizon Europe | https://ec.europa.eu/info/funding-tenders/opportunities/ | Weekly |

Configuration

Create `config.json` file to customize analysis parameters:

{
  "sources": {
    "nih": {
      "enabled": true,
      "base_url": "https://reporter.nih.gov/",
      "max_results": 1000
    },
    "nsf": {
      "enabled": true,
      "base_url": "https://www.nsf.gov/awardsearch/",
      "max_results": 1000
    },
    "horizon_europe": {
      "enabled": true,
      "base_url": "https://ec.europa.eu/info/funding-tenders/",
      "max_results": 500
    }
  },
  "nlp": {
    "language": "en",
    "min_word_length": 3,
    "max_topics": 20,
    "stop_words": ["research", "study", "project"]
  },
  "forecast": {
    "method": "lda_trend",
    "confidence_level": 0.95,
    "years_ahead": 5
  }
}

Output Format

JSON Report Structure

{
  "metadata": {
    "generated_at": "2024-01-15T10:30:00Z",
    "data_period": "2023-07-01 to 2024-01-01",
    "sources": ["nih", "nsf", "horizon_europe"],
    "total_projects": 15420
  },
  "trend_analysis": {
    "top_keywords": [
      {"term": "artificial intelligence", "frequency": 342, "growth": 0.45},
      {"term": "climate change", "frequency": 298, "growth": 0.32}
    ],
    "emerging_topics": [
      {"topic": "Large Language Models", "projects": 89, "trend": "rising"},
      {"topic": "Carbon Capture", "projects": 156, "trend": "stable"}
    ],
    "funding_shifts": {
      "increasing": ["AI/ML", "Climate Tech", "Quantum Computing"],
      "decreasing": ["Traditional Materials", "Fossil Fuels Research"]
    }
  },
  "forecast": {
    "2025": {
      "predicted_hot_topics": ["Generative AI", "Gene Editing", "Fusion Energy"],
      "confidence": 0.87
    },
    "2026-2029": {
      "long_term_trends": ["AGI Safety", "Personalized Medicine", "Space Mining"],
      "confidence": 0.72
    }
  }
}

Architecture

funding-trend-forecaster/
├── scripts/
│   ├── main.py              # Main entry
│   ├── collectors/          # Data collection module
│   │   ├── __init__.py
│   │   ├── nih_collector.py
│   │   ├── nsf_collector.py
│   │   └── horizon_collector.py
│   ├── analyzers/           # NLP analysis module
│   │   ├── __init__.py
│   │   ├── text_processor.py
│   │   ├── topic_modeler.py
│   │   └── trend_detector.py
│   ├── predictors/          # Prediction module
│   │   ├── __init__.py
│   │   └── trend_forecaster.py
│   └── utils/               # Utility module
│       ├── __init__.py
│       ├── config.py
│       └── visualizer.py
├── data/                    # Data storage
│   ├── raw/
│   └── processed/
├── output/                  # Output directory
├── config.json              # Configuration file
├── requirements.txt         # Python dependencies
└── SKILL.md                 # This document

Roadmap

License

MIT License - See LICENSE file in project root directory

---

*Generated by OpenClaw Agent | Skill ID: 200*

Risk Assessment

| Risk Indicator | Assessment | Level |

|----------------|------------|-------|

| Code Execution | Python scripts with tools | High |

| Network Access | External API calls | High |

| File System Access | Read/write data | Medium |

| Instruction Tampering | Standard prompt guidelines | Low |

| Data Exposure | Data handled securely | Medium |

Security Checklist

Prerequisites

# Python dependencies
pip install -r requirements.txt

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