Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add K-Dense-AI/scientific-agent-skills --skill usfiscaldatagit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/usfiscaldata)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/usfiscaldata"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/usfiscaldata/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/usfiscaldata"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/usfiscaldata.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 15 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 31 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.02419 |
| Opus 5.5 | $0.00027 | $0.00968 |
| Sonnet 5.5 | $0.00013 | $0.00484 |
| Haiku 4.5 | $0.00007 | $0.00242 |
Grade A, and why
usfiscaldata scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 26d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.get(f"{BASE_URL}/v2/accounting/od/debt_to_penny", params={ Copies of this mod
2 near-identical copies found in the catalogue:
- usfiscaldata — 100% identical, 43 lines differ
- usfiscaldata — 94% identical, 50 lines differ
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 the current dataset catalog 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
}, 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"])
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
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 26d ago Changed · +15 lines 7cdcb756489a
- 27d ago Changed · +2 lines d61f538c0f45
- 1mo ago Changed 610c4547e5aa
- 1mo ago First seen · 189 lines · 67 tokens per session scan A f48e2b5e8ed2
usfiscaldata is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 2,419 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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