usfiscaldata — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited usfiscaldata (Agent Skill) and scored it 65/100 (yellow). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 4 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 4 flagged
The text {match} is the classic direct prompt-injection phrasing. Placed in a skill body that the agent reads as trusted instructions, it tries to make the agent abandon its prior rules and follow whatever comes next — a full system-prompt override.
ignore/disregard/forget … previous instructions sentence.Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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 via the dataset search. Verify endpoint paths on each dataset's API Quick Guide — paths change over time.
uv pip install requests pandasimport 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"])None required. The API is fully open and free.
| 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"| 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 |
| 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) | Monthly |
| 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 |
| 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 |
| 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 |
{
"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".
Use the bounded fetch_all() helper in 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"])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"
})~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.