An advanced AI agent skill for navigating the Federal Reserve Economic Data (FRED) database using natural language and dual-layer validation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install fred-navigator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install fred-navigator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is FRED Navigator?
FRED Navigator provides a robust framework for AI agents to interact with the Federal Reserve Economic Data (FRED) ecosystem. By leveraging the fredapi and custom scripts, this skill enables seamless navigation through thousands of economic categories and series. It bridges the gap between complex hierarchical data structures and natural language queries, ensuring that developers and researchers can access precise economic indicators with high confidence and reliability using Openclaw Skills.
The skill is designed to handle the ambiguity of economic terminology by using intent recognition and semantic validation. This ensures that when a user asks for broad terms like inflation or employment, the agent can map these to the exact series IDs used by the St. Louis Fed, providing a professional-grade interface for economic research and data science workflows.
FRED Navigator Use Cases
- Researching specific economic indicators like GDP, CPI, or unemployment rates using natural language prompts.
- Hierarchical browsing of FRED categories to discover related economic datasets for comparative analysis.
- Automating the retrieval of time-series data for financial modeling, forecasting, and data visualization.
- Validating user intent against structured economic data categories to reduce retrieval errors in automated reports.
How FRED Navigator Works
- The user provides a direct category ID, series ID, or a natural language query describing the economic data they need.
- For natural language queries, the AI agent performs intent recognition to map the request to the most relevant FRED categories.
- The system performs structural validation against local reference files to ensure the category exists and contains active data series.
- Semantic validation compares the user query with category paths to ensure relevance, requesting user confirmation if the confidence score is low.
- Once validated, the skill retrieves series metadata and time-series data using sandboxed Python scripts and the official FRED API.
FRED Navigator Setup
To integrate this skill into your workflow, ensure you have a valid FRED API key from the St. Louis Fed website. Follow these steps to configure the environment:
# Set the environment variable for API access
export FRED_API_KEY='your_api_key_here'
# Precompute category paths for faster navigation
python scripts/build_paths.py
# Test the connection by checking a root category
python scripts/fred_query.py check-category 0
FRED Navigator Data Schema & Taxonomy
The skill utilizes a structured reference system to optimize lookup speed and accuracy within Openclaw Skills:
| Component | Description | Format |
|---|---|---|
| fred_categories_tree.json | Hierarchical tree of all FRED categories | Recursive JSON |
| fred_categories_flat.json | Flat list for fuzzy name matching | Array of Objects |
| category_paths.json | Precomputed paths for breadcrumb navigation | ID-keyed Dictionary |
| synonyms.json | Mapping of economic concepts to FRED terms | Key-Value List |
| fred_query.py | Core script for API interaction and filtering | Python Script |
FRED Navigator Advanced Features
- Double validation logic that combines structural integrity checks with semantic relevance scoring to prevent hallucinations.
- Secure sandbox execution for all Python-based querying to maintain environment isolation.
- Support for fuzzy matching against flat category files to handle minor typos or variations in economic terminology.
- Hierarchical browsing support that allows agents to traverse deep category trees within Openclaw Skills.
- Automated failure handling that provides the top 5 closest candidates when a query is ambiguous.
SKILL.md
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