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pratyushchauhan

FRED Data Viz

by Pratyush Chauhan · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install fred-data-viz
Description
Create publication-ready economic comparison charts from Federal Reserve Economic Data (FRED). Use when the user needs to visualize, compare, or analyze econ...
README (SKILL.md)

FRED Data Visualization

Create indexed comparison charts from Federal Reserve Economic Data (FRED) series.

Quick Start

For a simple two-series comparison:

python3 scripts/fred_chart.py --config chart_config.json --output chart.png

Workflow

1. Identify the FRED series

Find series IDs at fred.stlouisfed.org:

Indicator Common Series
Real GDP GDPC1
GDP Per Capita A939RX0Q048SBEA (or compute from GDPC1 + POPTHM)
Compensation Per Hour COMPRNFB
Median Weekly Earnings LES1252881600Q
Corporate Profits CP
CPI CPIAUCSL
Unemployment UNRATE

2. Create config JSON

{
  "title": "USA: GDP vs Wages (1959 = 100)",
  "series": [
    {
      "id": "A939RX0Q048SBEA",
      "label": "Real GDP Per Capita",
      "color": "#2E86AB"
    },
    {
      "id": "COMPRNFB",
      "label": "Real Compensation Per Hour",
      "color": "#F18F01"
    }
  ],
  "start_date": "1959-01-01",
  "end_date": "2026-12-31",
  "fill_gap": true,
  "annotations": [
    {
      "date": "1971-08-15",
      "label": "Nixon Shock",
      "position": "top",
      "y": 140
    }
  ]
}

Config fields:

  • title: Chart title
  • series[].id: FRED series ID
  • series[].label: Legend label
  • series[].color: Optional hex color
  • start_date/end_date: Date range filter
  • fill_gap: Shade area between first two series (default: true)
  • annotations[].date: Event date (YYYY-MM-DD)
  • annotations[].label: Event text (supports newlines with \ )
  • annotations[].position: "top" or "bottom"
  • annotations[].y: Vertical position for label placement

3. Generate chart

python3 scripts/fred_chart.py --config my_chart.json --output my_chart.png

Advanced: GDP Per Capita Calculation

When FRED lacks a direct GDP per capita series, compute it from GDP (GDPC1) and population (POPTHM):

# gdp_pc = (GDP in billions * 1e9) / (Population in thousands * 1e3)
# Or use the pre-computed series: A939RX0Q048SBEA (available from 1979)
# For earlier dates, manual calculation required

Tips

  • Indexing: All series are automatically indexed to their first observation (set to 100) for fair comparison
  • Annotations: Use \ for multi-line labels. Position alternates top/bottom to avoid overlap
  • Zooming: Use start_date/end_date to focus on specific eras (e.g., 1959-1985)
  • Colors: Choose contrasting colors. Blue (#2E86AB) and orange (#F18F01) work well
  • Multiple series: Up to 5 series supported. Gap fill only applies to first two

Common Chart Patterns

Pattern Series Use Case
Productivity-Pay Gap A939RX0Q048SBEA, COMPRNFB Show worker compensation vs economic output
Wage-Inflation Comparison CES0500000003, CPIAUCSL Real vs nominal wage growth
Profit-Wage Divergence CP, COMPRNFB Where surplus goes
Historical Events Any + annotations Annotated economic timeline
Usage Guidance
Before installing, note that the helper contacts FRED over the network, depends on pandas and matplotlib being available, and writes to the output path you provide. Avoid pointing the output at an important existing file unless you intend to overwrite it.
Capability Assessment
Purpose & Capability
The stated purpose is FRED data visualization, and the Python helper fetches public FRED CSV data, indexes series, plots them, and writes a chart image as requested.
Instruction Scope
Runtime instructions are explicit: create a JSON config and run the chart script. There are no prompt overrides, hidden agent instructions, or unrelated automation.
Install Mechanism
No installer, package manager action, autorun hook, or persistence setup is present; the artifact consists of a skill file and one helper script.
Credentials
Network access to fred.stlouisfed.org, reading a user-supplied config file, and writing a user-selected output image are proportionate to the charting purpose.
Persistence & Privilege
No credential access, privilege escalation, background worker, broad local indexing, or persistent state is implemented.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install fred-data-viz
  3. After installation, invoke the skill by name or use /fred-data-viz
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: Create indexed economic comparison charts from FRED data with annotations and gap visualization
Metadata
Slug fred-data-viz
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is FRED Data Viz?

Create publication-ready economic comparison charts from Federal Reserve Economic Data (FRED). Use when the user needs to visualize, compare, or analyze econ... It is an AI Agent Skill for Claude Code / OpenClaw, with 47 downloads so far.

How do I install FRED Data Viz?

Run "/install fred-data-viz" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is FRED Data Viz free?

Yes, FRED Data Viz is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does FRED Data Viz support?

FRED Data Viz is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created FRED Data Viz?

It is built and maintained by Pratyush Chauhan (@pratyushchauhan); the current version is v1.0.0.

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