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 LeonChaoX/qinyan-academic-skills --skill fred-economic-datagit clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-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/leonchaox/qinyan-academic-skills/fred-economic-data)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/fred-economic-data"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/fred-economic-data/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/leonchaox/qinyan-academic-skills/fred-economic-data"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/fred-economic-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00075 | $0.02884 |
| Opus 5 | $0.00037 | $0.01442 |
| Sonnet 5 | $0.00015 | $0.00577 |
| Haiku 4.5 | $0.00007 | $0.00288 |
Grade A, and why
fred-economic-data 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 8d 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.
response = requests.get( This is a copy
94% identical to fred-economic-data — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 434 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FRED Economic Data Access
Overview
Access comprehensive economic data through FRED (Federal Reserve Economic Data), a database maintained by the Federal Reserve Bank of St. Louis containing over 800,000 economic time series from over 100 sources.
Key capabilities:
- Query economic time series data (GDP, unemployment, inflation, interest rates)
- Search and discover series by keywords, tags, and categories
- Access historical data and vintage (revision) data via ALFRED
- Retrieve release schedules and data publication dates
- Map regional economic data with GeoFRED
- Apply data transformations (percent change, log, etc.)
API Key Setup
Required: All FRED API requests require an API key.
- Create an account at https://fredaccount.stlouisfed.org
- Log in and request an API key through the account portal
- Set as environment variable:
export FRED_API_KEY="your_32_character_key_here"
Or in Python:
import os
os.environ["FRED_API_KEY"] = "your_key_here"
Quick Start
Using the FREDQuery Class
from scripts.fred_query import FREDQuery
# Initialize with API key
fred = FREDQuery(api_key="YOUR_KEY") # or uses FRED_API_KEY env var
# Get GDP data
gdp = fred.get_series("GDP")
print(f"Latest GDP: {gdp['observations'][-1]}")
# Get unemployment rate observations
unemployment = fred.get_observations("UNRATE", limit=12)
for obs in unemployment["observations"]:
print(f"{obs['date']}: {obs['value']}%")
# Search for inflation series
inflation_series = fred.search_series("consumer price index")
for s in inflation_series["seriess"][:5]:
print(f"{s['id']}: {s['title']}")
Direct API Calls
import requests
import os
API_KEY = os.environ.get("FRED_API_KEY")
BASE_URL = "https://api.stlouisfed.org/fred"
# Get series observations
response = requests.get(
f"{BASE_URL}/series/observations",
params={
"api_key": API_KEY,
"series_id": "GDP",
"file_type": "json"
}
)
data = response.json()
What ships with it
9 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.
- 8d ago First seen · 434 lines · 75 tokens per session scan A 8b0d1e8a16af
fred-economic-data is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (880 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 2,884 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 94% identical to fred-economic-data, differing in 6 lines, and is treated as a copy.
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