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/usfiscaldata

Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds,

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$ npx -y skills add k-dense-ai/claude-scientific-skills --skill usfiscaldata --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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  • Slash command/usfiscaldata

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Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds,

SKILL.md

usfiscaldata.SKILL.md
name: usfiscaldata
description: Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.
license: MIT
allowed-tools: Read Write Edit Bash
metadata:
  version: "1.3"
  skill-author: K-Dense Inc.

U.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`

Browse [54 datasets and 179 data tables](https://fiscaldata.treasury.gov/datasets/) via the dataset search. Verify endpoint paths on each dataset's API Quick Guide — paths change over time.

Installation

uv pip install requests pandas

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/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` (18 tables — see [datasets-fiscal.md](references/datasets-fiscal.md)) | 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 | | Treasury Securities Buybacks | `/v1/accounting/od/buybacks_operations` | As Needed |

Savings Bonds

| Dataset | Endpoint | Frequency | |---------|----------|-----------| | I Bonds Interest Rates | `/v1/accounting/od/i_bonds_interest_rates` | Semi-Annual | | Savings Bonds Issues, Redemptions & Maturities | `/v1/accounting/od/savings_bonds_report` | 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

Use the bounded `fetch_all()` helper in [parameters.md](references/parameters.md). For small result sets, a single request with `page[size]=10000` may suffice when `meta.total-pages` is 1.

# Single-page fetch when total-pages == 1
params = {"sort": "-record_date", "page[size]": 10000}
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_outstanding", params=params)
result = resp.json()
if result["meta"]["total-pages"] > 1:
    raise ValueError("Use fetch_all() from parameters.md for multi-page results")
df = pd.DataFrame(result["data"])

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](references/api-basics.md)** — URL structure, HTTP methods, versioning, data types
  • **
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