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FRED Data Viz

作者 Pratyush Chauhan · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install fred-data-viz
功能描述
Create publication-ready economic comparison charts from Federal Reserve Economic Data (FRED). Use when the user needs to visualize, compare, or analyze econ...
使用说明 (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
安全使用建议
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.
能力评估
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.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install fred-data-viz
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /fred-data-viz 触发
  4. 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
v1.0.0
Initial release: Create indexed economic comparison charts from FRED data with annotations and gap visualization
元数据
Slug fred-data-viz
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

FRED Data Viz 是什么?

Create publication-ready economic comparison charts from Federal Reserve Economic Data (FRED). Use when the user needs to visualize, compare, or analyze econ... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 47 次。

如何安装 FRED Data Viz?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install fred-data-viz」即可一键安装,无需额外配置。

FRED Data Viz 是免费的吗?

是的,FRED Data Viz 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

FRED Data Viz 支持哪些平台?

FRED Data Viz 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 FRED Data Viz?

由 Pratyush Chauhan(@pratyushchauhan)开发并维护,当前版本 v1.0.0。

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