fred-economic-data

fred-economic-data is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 75 tokens per session (2,795 once invoked), scanned A, original, Apache-2.0.

A programming guide to FRED, the Federal Reserve Bank of St. Louis database of economic time series. It provides historical and revised data such as GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional indicators.

In plain words
What is it for?
Use it to search and retrieve economic series, compare trends, access release schedules and revised data, transform observations, and work with regional statistics.
Why use it?
It avoids collecting economic measurements from many sources and makes historical revisions and data transformations easier to handle.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to search and retrieve economic series, compare trends, access release schedules and revised data, transform observations, and work with regional statistics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/synthetic-sciences/openscience/fred-economic-data
About the project

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.

synthetic-sciences/openscience · 4,243 stars · on GitHub · openscience.sh

Install

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.

Any agent
npx skills add synthetic-sciences/openscience --skill fred-economic-data
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for fred-economic-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/fred-economic-data/github.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/fred-economic-data)
Your own site
<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/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.

agentmods 80×15 button for fred-economic-data

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,795 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, 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 74
    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.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.02795
Opus 5.5 $0.00030 $0.01118
Sonnet 5.5 $0.00015 $0.00559
Haiku 4.5 $0.00007 $0.00280

Measured 1mo ago against content hash be38e4d7c8de, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/fred_examples.py, scripts/fred_query.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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(
Origin

Copies of this mod

4 near-identical copies found in the catalogue:

backend/cli/skills/databases/fred-economic-data/SKILL.md · 438 lines

How it starts

The opening of the file, as written. The whole thing — 438 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.

  1. Create an account at https://fredaccount.stlouisfed.org
  2. Log in and request an API key through the account portal
  3. 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()

Read the full file on GitHub · 438 lines

Files

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.

Changes

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.

  1. 1mo ago Changed · +6 lines be38e4d7c8de
  2. 1mo ago First seen · 432 lines · 75 tokens per session scan A 3d0f614f7cb4

Subscribe to this mod's changes

fred-economic-data is a skill published in the GitHub repository synthetic-sciences/openscience (4,243 stars, last pushed yesterday), licensed Apache-2.0. It adds 75 tokens to every session and 2,795 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.

Related

Other skills, from other repositories

travel-planner

Creates comprehensive travel itineraries with destination analysis, budget planning, transportation, accommodation, and local insights. Use when planning trips, organizing travel schedules, or seeking destination recommendations.

alleneee/skill-agent · 38 tokens

polymarket

Query Polymarket: markets, prices, orderbooks, history.

StarryCod/cogitum · 18 tokens

company-profile

One-page company profile slide, four quadrants of overview, financial summary, share price chart and key facts. Also the compact profile paragraph when it has to sit inside a memo, a deck or a chat reply. Triggers on one-page profile, tear sheet, company snapshot, profile slide, quick profile of [company].

ginlix-ai/LangAlpha · 68 tokens

idea-generation

Find long and short candidates across a universe when no name is on the table yet: mandate, universe validation, archetype screens, thematic sweep, triage, idea cards, idea log. Triggers on idea generation, stock screen, find ideas, what looks interesting, screen for, new ideas, pitch me something.

ginlix-ai/LangAlpha · 67 tokens

onboarding

First run after the user saves their investing profile: import holdings and watchlists from the brokerage they connected or the screenshots they attached, then propose three first tasks (one on their portfolio or watchlist, one from their profile, one on today's market) and do the one they pick.

ginlix-ai/LangAlpha · 60 tokens

trade-pitch

Turn finished analysis on one named security into a position: variant view, falsifiable evidence, scenario tree, expression, risk and monitoring. Triggers on pitch this name, should I buy, a long idea or short idea on a named stock, position, trade recommendation, risk reward.

ginlix-ai/LangAlpha · 61 tokens