About usfiscaldata
usfiscaldata is a skill for querying the U.S. Treasury Fiscal Data REST API, which provides federal financial datasets without authentication. It explains endpoints, query parameters, response handling, and examples for working with fiscal data.
Key features
- 01Debt and Treasury statement datasets
- 02Interest and exchange rate data
- 03Securities and savings bond data
- 04Field selection, filtering, and sorting
- 05Pagination and response handling
- 06Python, R, and pandas examples
Use cases
- 01Checking current and historical federal debt
- 02Analyzing Treasury cash flows and statements
- 03Retrieving exchange rates or interest rates
- 04Exploring auction and savings bond data
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ crazymsn/ academic-skills --skill usfiscaldataAfter installing, type/usfiscaldatain Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/api-basics.mddatasets-debt.mddatasets-fiscal.mddatasets-interest-rates.mddatasets-securities.mdexamples.mdparameters.mdresponse-format.mdDetails
From the project READMEU.S. Treasury Fiscal Data API
Free, open REST API from the U.S. Department of the Treasury for federal financial data. No API key or registration required.
Base URL: https://api.fiscaldata.treasury.gov/services/api/fiscal_service
Quick Start
import requests
import pandas as pd
BASE_URL = "https://api.fiscaldata.treasury.gov/services/api/fiscal_service"
# Get the current national debt (Debt to the Penny)
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_to_penny", params={
"sort": "-record_date",
"page[size]": 1
})
data = resp.json()["data"][0]
print(f"Total public debt as of {data['record_date']}: ${float(data['tot_pub_debt_out_amt']):,.0f}")
# Get Treasury exchange rates for recent quarters
resp = requests.get(f"{BASE_URL}/v1/accounting/od/rates_of_exchange", params={
"fields": "country_currency_desc,exchange_rate,record_date",
"filter": "record_date:gte:2024-01-01",
"sort": "-record_date",
"page[size]": 100
})
df = pd.DataFrame(resp.json()["data"])
Authentication
None required. The API is fully open and free.
Core Parameters
| Parameter | Example | Description |
|---|---|---|
fields= | fields=record_date,tot_pub_debt_out_amt | Select specific columns |
filter= | filter=record_date:gte:2024-01-01 | Filter records |
sort= | sort=-record_date | Sort (prefix - for descending) |
format= | format=json | Output format: json, csv, xml |
page[size]= | page[size]=100 | Records per page (default 100) |
page[number]= | page[number]=2 | Page index (starts at 1) |
Filter operators: lt, lte, gt, gte, eq, in
# Multiple filters separated by comma
"filter=country_currency_desc:in:(Canada-Dollar,Mexico-Peso),record_date:gte:2024-01-01"
Key Datasets & Endpoints
Debt
| Dataset | Endpoint | Frequency |
|---|---|---|
| Debt to the Penny | /v2/accounting/od/debt_to_penny | Daily |
| Historical Debt Outstanding | /v2/accounting/od/historical_debt_outstanding | Annual |
| Schedules of Federal Debt | /v1/accounting/od/schedules_fed_debt | Monthly |
Daily & Monthly Statements
| Dataset | Endpoint | Frequency |
|---|---|---|
| DTS Operating Cash Balance | /v1/accounting/dts/operating_cash_balance | Daily |
| DTS Deposits & Withdrawals | /v1/accounting/dts/deposits_withdrawals_operating_cash | Daily |
| Monthly Treasury Statement (MTS) | /v1/accounting/mts/mts_table_1 (16 tables) | Monthly |
Interest Rates & Exchange
| Dataset | Endpoint | Frequency |
|---|---|---|
| Average Interest Rates on Treasury Securities | /v2/accounting/od/avg_interest_rates | Monthly |
| Treasury Reporting Rates of Exchange | /v1/accounting/od/rates_of_exchange | Quarterly |
| Interest Expense on Public Debt | /v2/accounting/od/interest_expense | Monthly |
Securities & Auctions
| Dataset | Endpoint | Frequency |
|---|---|---|
| Treasury Securities Auctions Data | /v1/accounting/od/auctions_query | As Needed |
| Treasury Securities Upcoming Auctions | /v1/accounting/od/upcoming_auctions | As Needed |
| Average Interest Rates | /v2/accounting/od/avg_interest_rates | Monthly |
Savings Bonds
| Dataset | Endpoint | Frequency |
|---|---|---|
| I Bonds Interest Rates | /v2/accounting/od/i_bond_interest_rates | Semi-Annual |
| U.S. Treasury Savings Bonds: Issues, Redemptions & Maturities | /v1/accounting/od/sb_issues_redemptions | Monthly |
Response Structure
{
"data": [...],
"meta": {
"count": 100,
"total-count": 3790,
"total-pages": 38,
"labels": {"field_name": "Human Readable Label"},
"dataTypes": {"field_name": "STRING|NUMBER|DATE|CURRENCY"},
"dataFormats": {"field_name": "String|10.2|YYYY-MM-DD"}
},
"links": {"self": "...", "first": "...", "prev": null, "next": "...", "last": "..."}
}
Note: All values are returned as strings. Convert as needed (e.g., float(), pd.to_datetime()). Null values appear as the string "null".
Common Patterns
Load all pages into a DataFrame
def fetch_all_pages(endpoint, params=None):
params = params or {}
params["page[size]"] = 10000 # max size to minimize requests
resp = requests.get(f"{BASE_URL}{endpoint}", params=params)
result = resp.json()
df = pd.DataFrame(result["data"])
return df
Aggregation (automatic sum)
Omitting grouping fields triggers automatic aggregation:
# Sum all deposits/withdrawals by record_date and transaction type
resp = requests.get(f"{BASE_URL}/v1/accounting/dts/deposits_withdrawals_operating_cash", params={
"fields": "record_date,transaction_type,transaction_today_amt"
})
Reference Files
- api-basics.md — URL structure, HTTP methods, versioning, data types
- parameters.md — All parameters with detailed examples and edge cases
- datasets-debt.md — Debt datasets: Debt to the Penny, Historical Debt, Schedules of Federal Debt, TROR
- datasets-fiscal.md — Daily Treasury Statement, Monthly Treasury Statement, revenue, spending
- datasets-interest-rates.md — Average interest rates, exchange rates, TIPS/CPI, certified interest rates
- datasets-securities.md — Treasury auctions, savings bonds, SLGS, buybacks
- response-format.md — Response objects, error handling, pagination, response codes
- examples.md — Python, R, and pandas code examples for common use cases