Api Data Fetcher
- 194 installs
- 590 repo stars
- Updated June 23, 2026
- meleantonio/awesome-econ-ai-stuff
Fetch external economic and market API datasets, handle auth and pagination, normalize JSON responses, and deliver clean records into analysis scripts or agent workflows.
About
The api-data-fetcher skill from meleantonio/awesome-econ-ai-stuff helps Claude integrate external economic and market data APIs: configuring authenticated requests, traversing paginated endpoints, normalizing heterogeneous JSON into consistent records, and piping cleaned datasets into CLI tools, analysis pipelines, or agent workflows.
- External economic API ingestion
- Auth, pagination, and rate-limit handling
- Response normalization and typing
- Dataset cleaning for downstream analysis
- Agent-ready structured data feeds
Api Data Fetcher by the numbers
- 194 all-time installs (skills.sh)
- +6 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #2,058 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 194 |
|---|---|
| repo stars | ★ 590 |
| Last updated | June 23, 2026 |
| Repository | meleantonio/awesome-econ-ai-stuff ↗ |
What it does
Fetch external economic and market API datasets, handle auth and pagination, normalize JSON responses, and deliver clean records into analysis scripts or agent workflows.
Files
API Data Fetcher
Purpose
This skill helps economists fetch data from major economic data APIs including FRED (Federal Reserve Economic Data), World Bank, IMF, BLS, and OECD. It generates clean, documented Python code with proper error handling.
When to Use
- Downloading macroeconomic indicators
- Building custom datasets from multiple sources
- Automating data updates for ongoing projects
- Fetching cross-country panel data
Instructions
Step 1: Identify Data Requirements
Ask the user: 1. What data do you need? (GDP, unemployment, inflation, etc.) 2. What time period and frequency? 3. What countries/regions? 4. Preferred output format? (CSV, DataFrame, etc.)
Step 2: Select Appropriate API
| Data Type | Best Source | Package |
|---|---|---|
| US macro | FRED | fredapi |
| Global development | World Bank | wbdata |
| Labor statistics | BLS | bls |
| Cross-country | OECD | pandasdmx |
| Financial | Yahoo Finance | yfinance |
Step 3: Generate Clean Code
Include:
- API key handling (environment variables)
- Error handling for API failures
- Data cleaning and formatting
- Documentation of series definitions
Example Output
"""
Economic Data Fetcher
=====================
Downloads macroeconomic data from FRED and World Bank APIs.
Requires: fredapi, wbdata, pandas
Setup: Set FRED_API_KEY environment variable
Get a free key from: https://fred.stlouisfed.org/docs/api/api_key.html
"""
import os
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Optional, Dict
# ============================================
# FRED Data Fetcher
# ============================================
def fetch_fred_series(
series_ids: List[str],
start_date: str = "2000-01-01",
end_date: Optional[str] = None,
api_key: Optional[str] = None
) -> pd.DataFrame:
"""
Fetch time series data from FRED.
Parameters
----------
series_ids : list of str
FRED series IDs (e.g., ['GDP', 'UNRATE', 'CPIAUCSL'])
start_date : str
Start date in YYYY-MM-DD format
end_date : str, optional
End date (defaults to today)
api_key : str, optional
FRED API key (defaults to FRED_API_KEY env var)
Returns
-------
pd.DataFrame
DataFrame with date index and series as columns
Example
-------
>>> df = fetch_fred_series(['GDP', 'UNRATE'], '2010-01-01')
"""
try:
from fredapi import Fred
except ImportError:
raise ImportError("Install fredapi: pip install fredapi")
# Get API key
api_key = api_key or os.environ.get('FRED_API_KEY')
if not api_key:
raise ValueError(
"FRED API key required. Set FRED_API_KEY environment variable "
"or pass api_key parameter. Get a key at: "
"https://fred.stlouisfed.org/docs/api/api_key.html"
)
fred = Fred(api_key=api_key)
end_date = end_date or datetime.now().strftime('%Y-%m-%d')
# Fetch each series
data = {}
for series_id in series_ids:
try:
series = fred.get_series(
series_id,
observation_start=start_date,
observation_end=end_date
)
data[series_id] = series
print(f"✓ Downloaded {series_id}")
except Exception as e:
print(f"✗ Failed to download {series_id}: {e}")
# Combine into DataFrame
df = pd.DataFrame(data)
df.index.name = 'date'
return df
# Common FRED series for economists
FRED_SERIES = {
# GDP and Output
'GDP': 'Gross Domestic Product',
'GDPC1': 'Real GDP',
'GDPPOT': 'Real Potential GDP',
# Labor Market
'UNRATE': 'Unemployment Rate',
'PAYEMS': 'Total Nonfarm Payrolls',
'CIVPART': 'Labor Force Participation Rate',
# Prices
'CPIAUCSL': 'Consumer Price Index',
'PCEPI': 'PCE Price Index',
'CPILFESL': 'Core CPI',
# Interest Rates
'FEDFUNDS': 'Federal Funds Rate',
'DGS10': '10-Year Treasury Rate',
'T10Y2Y': '10Y-2Y Treasury Spread',
# Money and Credit
'M2SL': 'M2 Money Stock',
'TOTRESNS': 'Total Reserves',
}
# ============================================
# World Bank Data Fetcher
# ============================================
def fetch_world_bank_data(
indicators: Dict[str, str],
countries: List[str] = ['USA', 'GBR', 'DEU', 'FRA', 'JPN'],
start_year: int = 2000,
end_year: Optional[int] = None
) -> pd.DataFrame:
"""
Fetch indicator data from World Bank.
Parameters
----------
indicators : dict
Dict mapping indicator codes to names
e.g., {'NY.GDP.PCAP.CD': 'gdp_per_capita'}
countries : list of str
ISO 3-letter country codes
start_year : int
Start year
end_year : int, optional
End year (defaults to current year)
Returns
-------
pd.DataFrame
Panel data with country and year
Example
-------
>>> indicators = {
... 'NY.GDP.PCAP.CD': 'gdp_per_capita',
... 'SP.POP.TOTL': 'population'
... }
>>> df = fetch_world_bank_data(indicators, ['USA', 'GBR'])
"""
try:
import wbdata
except ImportError:
raise ImportError("Install wbdata: pip install wbdata")
end_year = end_year or datetime.now().year
all_data = []
for indicator_code, indicator_name in indicators.items():
try:
# Fetch data
data = wbdata.get_dataframe(
{indicator_code: indicator_name},
country=countries,
)
data = data.reset_index()
all_data.append(data)
print(f"✓ Downloaded {indicator_name}")
except Exception as e:
print(f"✗ Failed to download {indicator_name}: {e}")
# Merge all indicators
if all_data:
df = all_data[0]
for other_df in all_data[1:]:
df = df.merge(other_df, on=['country', 'date'], how='outer')
# Filter years
df['year'] = pd.to_datetime(df['date']).dt.year
df = df[(df['year'] >= start_year) & (df['year'] <= end_year)]
return df
return pd.DataFrame()
# Common World Bank indicators
WORLD_BANK_INDICATORS = {
# Income and Growth
'NY.GDP.PCAP.CD': 'GDP per capita (current US$)',
'NY.GDP.PCAP.KD.ZG': 'GDP per capita growth (%)',
'NY.GDP.MKTP.KD.ZG': 'GDP growth (%)',
# Population
'SP.POP.TOTL': 'Population, total',
'SP.URB.TOTL.IN.ZS': 'Urban population (%)',
# Trade
'NE.TRD.GNFS.ZS': 'Trade (% of GDP)',
'BX.KLT.DINV.WD.GD.ZS': 'FDI, net inflows (% of GDP)',
# Human Capital
'SE.XPD.TOTL.GD.ZS': 'Education expenditure (% of GDP)',
'SH.XPD.CHEX.GD.ZS': 'Health expenditure (% of GDP)',
# Inequality
'SI.POV.GINI': 'Gini index',
'SI.POV.DDAY': 'Poverty headcount ratio ($1.90/day)',
}
# ============================================
# Usage Example
# ============================================
if __name__ == "__main__":
# Example 1: Fetch US macro data from FRED
us_macro = fetch_fred_series(
series_ids=['GDP', 'UNRATE', 'CPIAUCSL', 'FEDFUNDS'],
start_date='2010-01-01'
)
print("\nUS Macro Data (FRED):")
print(us_macro.tail())
# Save to CSV
us_macro.to_csv('data/us_macro_fred.csv')
print("\nSaved to data/us_macro_fred.csv")
# Example 2: Fetch cross-country data from World Bank
indicators = {
'NY.GDP.PCAP.CD': 'gdp_per_capita',
'SP.POP.TOTL': 'population',
'NY.GDP.MKTP.KD.ZG': 'gdp_growth'
}
cross_country = fetch_world_bank_data(
indicators=indicators,
countries=['USA', 'GBR', 'DEU', 'FRA', 'JPN', 'CHN', 'IND', 'BRA'],
start_year=2000
)
print("\nCross-Country Data (World Bank):")
print(cross_country.head(10))
# Save to CSV
cross_country.to_csv('data/cross_country_wb.csv', index=False)
print("\nSaved to data/cross_country_wb.csv")Requirements
Python Packages
pip install fredapi wbdata pandasAPI Keys
- FRED: Free key from https://fred.stlouisfed.org/docs/api/api_key.html
- World Bank: No key required
- BLS: Free key from https://www.bls.gov/developers/
Set environment variables:
export FRED_API_KEY="your_key_here"Best Practices
1. Store API keys in environment variables - never hardcode 2. Add rate limiting for bulk downloads 3. Cache data locally to avoid repeated API calls 4. Document series definitions from the source 5. Check for revisions in real-time data
Common Pitfalls
- ❌ Hardcoding API keys in scripts
- ❌ Not handling API rate limits
- ❌ Ignoring data vintages/revisions
- ❌ Mixing data frequencies without proper handling
References
Changelog
v1.0.0
- Initial release with FRED and World Bank support
API Data Fetcher
Purpose
This skill helps economists fetch data from major economic data APIs including FRED (Federal Reserve Economic Data), World Bank, IMF, BLS, and OECD. It generates clean, documented Python code with proper error handling.
When to Use
- Downloading macroeconomic indicators
- Building custom datasets from multiple sources
- Automating data updates for ongoing projects
- Fetching cross-country panel data
Instructions
Step 1: Identify Data Requirements
Ask the user: 1. What data do you need? (GDP, unemployment, inflation, etc.) 2. What time period and frequency? 3. What countries/regions? 4. Preferred output format? (CSV, DataFrame, etc.)
Step 2: Select Appropriate API
| Data Type | Best Source | Package |
|---|---|---|
| US macro | FRED | fredapi |
| Global development | World Bank | wbdata |
| Labor statistics | BLS | bls |
| Cross-country | OECD | pandasdmx |
| Financial | Yahoo Finance | yfinance |
Step 3: Generate Clean Code
Include:
- API key handling (environment variables)
- Error handling for API failures
- Data cleaning and formatting
- Documentation of series definitions
Example Output
"""
Economic Data Fetcher
=====================
Downloads macroeconomic data from FRED and World Bank APIs.
Requires: fredapi, wbdata, pandas
Setup: Set FRED_API_KEY environment variable
Get a free key from: https://fred.stlouisfed.org/docs/api/api_key.html
"""
import os
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Optional, Dict
# ============================================
# FRED Data Fetcher
# ============================================
def fetch_fred_series(
series_ids: List[str],
start_date: str = "2000-01-01",
end_date: Optional[str] = None,
api_key: Optional[str] = None
) -> pd.DataFrame:
"""
Fetch time series data from FRED.
Parameters
----------
series_ids : list of str
FRED series IDs (e.g., ['GDP', 'UNRATE', 'CPIAUCSL'])
start_date : str
Start date in YYYY-MM-DD format
end_date : str, optional
End date (defaults to today)
api_key : str, optional
FRED API key (defaults to FRED_API_KEY env var)
Returns
-------
pd.DataFrame
DataFrame with date index and series as columns
Example
-------
>>> df = fetch_fred_series(['GDP', 'UNRATE'], '2010-01-01')
"""
try:
from fredapi import Fred
except ImportError:
raise ImportError("Install fredapi: pip install fredapi")
# Get API key
api_key = api_key or os.environ.get('FRED_API_KEY')
if not api_key:
raise ValueError(
"FRED API key required. Set FRED_API_KEY environment variable "
"or pass api_key parameter. Get a key at: "
"https://fred.stlouisfed.org/docs/api/api_key.html"
)
fred = Fred(api_key=api_key)
end_date = end_date or datetime.now().strftime('%Y-%m-%d')
# Fetch each series
data = {}
for series_id in series_ids:
try:
series = fred.get_series(
series_id,
observation_start=start_date,
observation_end=end_date
)
data[series_id] = series
print(f"✓ Downloaded {series_id}")
except Exception as e:
print(f"✗ Failed to download {series_id}: {e}")
# Combine into DataFrame
df = pd.DataFrame(data)
df.index.name = 'date'
return df
# Common FRED series for economists
FRED_SERIES = {
# GDP and Output
'GDP': 'Gross Domestic Product',
'GDPC1': 'Real GDP',
'GDPPOT': 'Real Potential GDP',
# Labor Market
'UNRATE': 'Unemployment Rate',
'PAYEMS': 'Total Nonfarm Payrolls',
'CIVPART': 'Labor Force Participation Rate',
# Prices
'CPIAUCSL': 'Consumer Price Index',
'PCEPI': 'PCE Price Index',
'CPILFESL': 'Core CPI',
# Interest Rates
'FEDFUNDS': 'Federal Funds Rate',
'DGS10': '10-Year Treasury Rate',
'T10Y2Y': '10Y-2Y Treasury Spread',
# Money and Credit
'M2SL': 'M2 Money Stock',
'TOTRESNS': 'Total Reserves',
}
# ============================================
# World Bank Data Fetcher
# ============================================
def fetch_world_bank_data(
indicators: Dict[str, str],
countries: List[str] = ['USA', 'GBR', 'DEU', 'FRA', 'JPN'],
start_year: int = 2000,
end_year: Optional[int] = None
) -> pd.DataFrame:
"""
Fetch indicator data from World Bank.
Parameters
----------
indicators : dict
Dict mapping indicator codes to names
e.g., {'NY.GDP.PCAP.CD': 'gdp_per_capita'}
countries : list of str
ISO 3-letter country codes
start_year : int
Start year
end_year : int, optional
End year (defaults to current year)
Returns
-------
pd.DataFrame
Panel data with country and year
Example
-------
>>> indicators = {
... 'NY.GDP.PCAP.CD': 'gdp_per_capita',
... 'SP.POP.TOTL': 'population'
... }
>>> df = fetch_world_bank_data(indicators, ['USA', 'GBR'])
"""
try:
import wbdata
except ImportError:
raise ImportError("Install wbdata: pip install wbdata")
end_year = end_year or datetime.now().year
all_data = []
for indicator_code, indicator_name in indicators.items():
try:
# Fetch data
data = wbdata.get_dataframe(
{indicator_code: indicator_name},
country=countries,
)
data = data.reset_index()
all_data.append(data)
print(f"✓ Downloaded {indicator_name}")
except Exception as e:
print(f"✗ Failed to download {indicator_name}: {e}")
# Merge all indicators
if all_data:
df = all_data[0]
for other_df in all_data[1:]:
df = df.merge(other_df, on=['country', 'date'], how='outer')
# Filter years
df['year'] = pd.to_datetime(df['date']).dt.year
df = df[(df['year'] >= start_year) & (df['year'] <= end_year)]
return df
return pd.DataFrame()
# Common World Bank indicators
WORLD_BANK_INDICATORS = {
# Income and Growth
'NY.GDP.PCAP.CD': 'GDP per capita (current US$)',
'NY.GDP.PCAP.KD.ZG': 'GDP per capita growth (%)',
'NY.GDP.MKTP.KD.ZG': 'GDP growth (%)',
# Population
'SP.POP.TOTL': 'Population, total',
'SP.URB.TOTL.IN.ZS': 'Urban population (%)',
# Trade
'NE.TRD.GNFS.ZS': 'Trade (% of GDP)',
'BX.KLT.DINV.WD.GD.ZS': 'FDI, net inflows (% of GDP)',
# Human Capital
'SE.XPD.TOTL.GD.ZS': 'Education expenditure (% of GDP)',
'SH.XPD.CHEX.GD.ZS': 'Health expenditure (% of GDP)',
# Inequality
'SI.POV.GINI': 'Gini index',
'SI.POV.DDAY': 'Poverty headcount ratio ($1.90/day)',
}
# ============================================
# Usage Example
# ============================================
if __name__ == "__main__":
# Example 1: Fetch US macro data from FRED
us_macro = fetch_fred_series(
series_ids=['GDP', 'UNRATE', 'CPIAUCSL', 'FEDFUNDS'],
start_date='2010-01-01'
)
print("\nUS Macro Data (FRED):")
print(us_macro.tail())
# Save to CSV
us_macro.to_csv('data/us_macro_fred.csv')
print("\nSaved to data/us_macro_fred.csv")
# Example 2: Fetch cross-country data from World Bank
indicators = {
'NY.GDP.PCAP.CD': 'gdp_per_capita',
'SP.POP.TOTL': 'population',
'NY.GDP.MKTP.KD.ZG': 'gdp_growth'
}
cross_country = fetch_world_bank_data(
indicators=indicators,
countries=['USA', 'GBR', 'DEU', 'FRA', 'JPN', 'CHN', 'IND', 'BRA'],
start_year=2000
)
print("\nCross-Country Data (World Bank):")
print(cross_country.head(10))
# Save to CSV
cross_country.to_csv('data/cross_country_wb.csv', index=False)
print("\nSaved to data/cross_country_wb.csv")Requirements
Python Packages
pip install fredapi wbdata pandasAPI Keys
- FRED: Free key from https://fred.stlouisfed.org/docs/api/api_key.html
- World Bank: No key required
- BLS: Free key from https://www.bls.gov/developers/
Set environment variables:
export FRED_API_KEY="your_key_here"Best Practices
1. Store API keys in environment variables - never hardcode 2. Add rate limiting for bulk downloads 3. Cache data locally to avoid repeated API calls 4. Document series definitions from the source 5. Check for revisions in real-time data
Common Pitfalls
- ❌ Hardcoding API keys in scripts
- ❌ Not handling API rate limits
- ❌ Ignoring data vintages/revisions
- ❌ Mixing data frequencies without proper handling
References
Changelog
v1.0.0
- Initial release with FRED and World Bank support
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