Fred Economic Data

What it does

When your agent uses it

• Macroeconomic analysis
• Financial research
• Economic forecasting
• Academic research requiring U.S

Example prompts

• “/fred-economic-data”

Requirements

• Python 3
• A credential in FRED_API_KEY
• A credential in YOUR_KEY

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

1. 1Create an account at https://fredaccount.stlouisfed.org
2. 2Log in and request an API key through the account portal
3. 3Set as environment variable

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 4 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • api.stlouisfed.org

  Also links to:

  • fred.stlouisfed.org
  • fredaccount.stlouisfed.org
  • geofred.stlouisfed.org
  • alfred.stlouisfed.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • FRED_API_KEY
  • API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fred Economic Data loads about 3k tokens when it runs, and up to ~23k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 728 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~95

When it runs · the whole SKILL.md, loaded when a task matches

~3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~23k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 728 words, ~2,997 tokens.

name

fred-economic-data

description

Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.

license

Unknown

metadata.skill-author

K-Dense Inc.

export FRED_API_KEY="your_32_character_key_here"

import os
os.environ["FRED_API_KEY"] = "your_key_here"

from scripts.fred_query import FREDQuery

# Initialize with API key
fred = FREDQuery(api_key="YOUR_KEY")  # or uses FRED_API_KEY env var

# Get GDP data
gdp = fred.get_series("GDP")
print(f"Latest GDP: {gdp['observations'][-1]}")

# Get unemployment rate observations
unemployment = fred.get_observations("UNRATE", limit=12)
for obs in unemployment["observations"]:
    print(f"{obs['date']}: {obs['value']}%")

# Search for inflation series
inflation_series = fred.search_series("consumer price index")
for s in inflation_series["seriess"][:5]:
    print(f"{s['id']}: {s['title']}")

import requests
import os

API_KEY = os.environ.get("FRED_API_KEY")
BASE_URL = "https://api.stlouisfed.org/fred"

# Get series observations
response = requests.get(
    f"{BASE_URL}/series/observations",
    params={
        "api_key": API_KEY,
        "series_id": "GDP",
        "file_type": "json"
    }
)
data = response.json()

# Get observations with transformations
obs = fred.get_observations(
    series_id="GDP",
    units="pch",  # percent change
    frequency="q",  # quarterly
    observation_start="2020-01-01"
)

# Search with filters
results = fred.search_series(
    "unemployment",
    filter_variable="frequency",
    filter_value="Monthly"
)

# Get root categories (category_id=0)
root = fred.get_category()

# Get Money Banking & Finance category and its series
category = fred.get_category(32991)
series = fred.get_category_series(32991)

# Get upcoming release dates
upcoming = fred.get_release_dates()

# Get GDP release info
gdp_release = fred.get_release(53)

# Find series with multiple tags
series = fred.get_series_by_tags(["gdp", "quarterly", "usa"])

# Get related tags
related = fred.get_related_tags("inflation")

# Get all sources
sources = fred.get_sources()

# Get Federal Reserve releases
fed_releases = fred.get_source_releases(source_id=1)

# Get state unemployment data
regional = fred.get_regional_data(
    series_group="1220",  # Unemployment rate
    region_type="state",
    date="2023-01-01",
    units="Percent",
    season="NSA"
)

# Get GeoJSON shapes
shapes = fred.get_shapes("state")

# Get GDP percent change from year ago
gdp_growth = fred.get_observations("GDP", units="pc1")

# Convert daily to monthly average
monthly = fred.get_observations(
    "DGS10",
    frequency="m",
    aggregation_method="avg"
)

# Get GDP as it was reported on a specific date
vintage_gdp = fred.get_observations(
    "GDP",
    realtime_start="2020-01-01",
    realtime_end="2020-01-01"
)

# Get all vintage dates for a series
vintages = fred.get_vintage_dates("GDP")

def get_economic_snapshot(fred):
    """Get current values of key indicators."""
    indicators = ["GDP", "UNRATE", "CPIAUCSL", "FEDFUNDS", "DGS10"]
    snapshot = {}

    for series_id in indicators:
        obs = fred.get_observations(series_id, limit=1, sort_order="desc")
        if obs.get("observations"):
            latest = obs["observations"][0]
            snapshot[series_id] = {
                "value": latest["value"],
                "date": latest["date"]
            }

    return snapshot

def compare_series(fred, series_ids, start_date):
    """Compare multiple series over time."""
    import pandas as pd

    data = {}
    for sid in series_ids:
        obs = fred.get_observations(
            sid,
            observation_start=start_date,
            units="pc1"  # Normalize as percent change
        )
        data[sid] = {
            o["date"]: float(o["value"])
            for o in obs["observations"]
            if o["value"] != "."
        }

    return pd.DataFrame(data)

def get_upcoming_releases(fred, days=7):
    """Get data releases in next N days."""
    from datetime import datetime, timedelta

    end_date = datetime.now() + timedelta(days=days)

    releases = fred.get_release_dates(
        realtime_start=datetime.now().strftime("%Y-%m-%d"),
        realtime_end=end_date.strftime("%Y-%m-%d"),
        include_release_dates_with_no_data="true"
    )

    return releases

def map_state_unemployment(fred, date):
    """Get unemployment by state for mapping."""
    data = fred.get_regional_data(
        series_group="1220",
        region_type="state",
        date=date,
        units="Percent",
        frequency="a",
        season="NSA"
    )

    # Get GeoJSON for mapping
    shapes = fred.get_shapes("state")

    return data, shapes

result = fred.get_observations("INVALID_SERIES")

if "error" in result:
    print(f"Error {result['error']['code']}: {result['error']['message']}")
elif not result.get("observations"):
    print("No data available")
else:
    # Process data
    for obs in result["observations"]:
        if obs["value"] != ".":  # Handle missing values
            print(f"{obs['date']}: {obs['value']}")

uv run python scripts/fred_examples.py

© lamm-mit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (scripts, references) in skills/fred-economic-data of lamm-mit/scienceclaw.

• SKILL.md
• references/api_basics.md
• references/categories.md
• references/geofred.md
• references/releases.md
• references/series.md
• references/sources.md
• references/tags.md
• scripts/__pycache__/fred_examples.cpython-313.pyc
• scripts/__pycache__/fred_query.cpython-313.pyc
• scripts/fred_examples.py
• scripts/fred_query.py

Open the folder on GitHubat commit ab9aba1

Used in 4 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in lamm-mit/scienceclaw, which our catalogue first saw on October 7, 2026.

• agent-skills-hub/agent-skills-hubskills/fred-economic-data
• foryourhealth111-pixel/Vibe-Skillsbundled/skills/fred-economic-data
• LeonChaoX/qinyan-academic-skillsskills/15-金融与经济数据/fred-economic-data
• franklee16/academic-research-skillsdata-sourcing/fred-economic-data