Usfiscaldata

What it does

When your agent uses it

• National debt (Debt to the Penny)
• Daily Treasury Statements
• Monthly Treasury Statements
• Treasury securities auctions

Example prompts

• “Use the usfiscaldata skill to query the U.S. An agent skill from K-Dense-AI/scientific-agent-skills”
• “/usfiscaldata”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.10+ with requests and pandas for Python examples; R with httr and jsonlite for R examples. Requires network access; no credentials.
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  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.fiscaldata.treasury.gov

  Also links to:

  • fiscaldata.treasury.gov
  • arxiv.org
  • doi.org
  • export.arxiv.org

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

• Credentials

  Names no API keys, tokens, secrets or passwords.

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

• Compatibility

  Requires Python 3.10+ with requests and pandas for Python examples; R with httr and jsonlite for R examples. Requires network access; no credentials.

  From compatibility in the SKILL.md frontmatter.

Context cost

Usfiscaldata loads about 2.4k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 694 words of instructions outside code blocks.

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

~86

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

~2.4k

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

~17k

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

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Write, Edit, Bash

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 694 words, ~2,402 tokens.

name

usfiscaldata

description

Queries 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.

allowed-tools

Read, Write, Edit, Bash

compatibility

Requires Python 3.10+ with requests and pandas for Python examples; R with httr and jsonlite for R examples. Requires network access; no credentials.

license

MIT

metadata.version

1.5

metadata.last-reviewed

2026-09-30

metadata.skill-author

K-Dense Inc.

uv pip install requests pandas

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
}, timeout=30)
resp.raise_for_status()
data = resp.json()["data"][0]
print(f"Total public debt as of {data['record_date']}: ${float(data['tot_pub_debt_out_amt']):,.0f}")

# Preview Treasury exchange-rate rows for recent quarters (first page only)
resp = requests.get(f"{BASE_URL}/v1/accounting/od/rates_of_exchange", params={
    "fields": "country_currency_desc,exchange_rate,record_date,effective_date",
    "filter": "record_date:gte:2024-01-01",
    "sort": "-record_date",
    "page[size]": 100
}, timeout=30)
resp.raise_for_status()
df = pd.DataFrame(resp.json()["data"])

# Multiple filters separated by comma
"filter=country_currency_desc:in:(Canada-Dollar,Mexico-Peso),record_date:gte:2024-01-01"

{
  "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": "..."}
}

# 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, timeout=30)
resp.raise_for_status()
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"])

# Preview full DTS rows, preserving account and category dimensions
resp = requests.get(f"{BASE_URL}/v1/accounting/dts/deposits_withdrawals_operating_cash", params={
    "filter": "record_date:eq:2024-01-16", "page[size]": 1000
}, timeout=30)
resp.raise_for_status()

© K-Dense-AI, MIT. 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 8 other files (references) in skills/usfiscaldata of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/api-basics.md
• references/datasets-debt.md
• references/datasets-fiscal.md
• references/datasets-interest-rates.md
• references/datasets-securities.md
• references/examples.md
• references/parameters.md
• references/response-format.md

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/usfiscaldata