synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and 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 agentmods add skills/synthetic-sciences/openscience/fred-economic-datanpx skills add synthetic-sciences/openscience --skill fred-economic-datagit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/fred-economic-data)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/fred-economic-data"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/fred-economic-data.svg" alt="Measured on agentmods" 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 | $0.02726 |
| Opus 5 | $0.00037 | $0.01363 |
| Sonnet 5 | $0.00015 | $0.00545 |
| Haiku 4.5 | $0.00007 | $0.00273 |
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 2d 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( Copies of this mod
4 near-identical copies found in the catalogue:
- fred-economic-data — 98% identical, 3 lines differ
- fred-economic-data — 94% identical, 6 lines differ
- fred-economic-data — 94% identical, 6 lines differ
- fred-economic-data — 94% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 432 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.
- 2d ago First seen · 432 lines · 75 tokens per session scan A 3d0f614f7cb4
fred-economic-data is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 75 tokens to every session and 2,726 once invoked, about $0.0004 per session on Opus 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.
Other skills, from other repositories
dcf-model
DCF valuation: free cash flow projections, WACC, terminal value, sensitivity analysis.
comps-analysis
Comparable company analysis: operating metrics, valuation multiples, peer benchmarking.
initiating-coverage
Full equity research initiation: company research, financial model, valuation, charts, 30-50 page report.
ui-design
Design-quality reference for financial-research visual output: typography, color, composition, and avoiding generic AI aesthetics.
onboarding
First-time user onboarding to set up investment profile, watchlists, portfolio, and preferences.
user-profile
Manage user profile including watchlists, portfolio, and preferences.