adaptyv
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user…
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,
$ npx -y skills add k-dense-ai/claude-scientific-skills --skill usfiscaldata --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/usfiscaldataContext preview
The summary Claude sees to decide when to auto-load this skill.
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,
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.
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.
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
})
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](references/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](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"])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"
})🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user…
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection,…
AlphaGenome API key, free for non-commercial use from deepmind.google.com/science/alphagenome. ALPHA_GENOME_API_KEY is accepted as an alternative spelling.
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP…
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data…
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree…