Agent skill

Fred Macro

API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).

Install Fred Macro

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install gauss314/skills fred-macro --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fred-macro .claude/skills/fred-macro && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .claude/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/gauss314/skills/tree/main/skills/fred-macro

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install gauss314/skills fred-macro --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/fred-macro .agents/skills/fred-macro && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .agents/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install gauss314/skills fred-macro --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/fred-macro .cursor/skills/fred-macro && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .cursor/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/gauss314/skills.git --path skills/fred-macro

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install gauss314/skills fred-macro --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/fred-macro .gemini/skills/fred-macro && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .gemini/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install gauss314/skills fred-macro

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/fred-macro .github/skills/fred-macro && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .github/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add gauss314/skills --skill fred-macro -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install gauss314/skills fred-macro --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/fred-macro .opencode/skills/fred-macro && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "fred-macro" agent skill from https://github.com/gauss314/skills/tree/main/skills/fred-macro into .opencode/skills/fred-macro/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-macro", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: fred-macro
GitHub stars: 246
Token cost: ~2.2k tokens
SKILL.md length: 669 words
Files: 6 (incl. scripts, references)
Skills in repo: 32
Repo updated: Jun 14, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).

Works in 3 steps: Descargar una serie (Python) → Buscar series por palabra clave → Usando pandas
Data & Analytics work in your project
SKILL.md covers Autenticación, Rate Limits, Endpoints Principales and Formato de Respuesta, plus 5 more sections
Runs Python scripts from its folder; reaches api.stlouisfed.org; needs API_KEY and FRED_API_KEY

What it does

Fred Macro is an agent skill from gauss314/skills. API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/API_REFERENCE.md`, `references/SERIES_REFERENCE.md` and `scripts/download_multiple.py`).

It sits in Data & Analytics. The repository describes itself as: Financial market data consumption skills for claude code and AI agents. The licence is MIT.

When your agent uses it

Data & Analytics work in your project

Example prompts

“/fred-macro”

Requirements

Python 3
A credential in API_KEY
A credential in FRED_API_KEY

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Descargar una serie (Python)
Buscar series por palabra clave
Usando pandas

What it can do on your machine

Read from SKILL.md and the folder at commit 5156f81. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- api.stlouisfed.org
Also links to:
- fred.stlouisfed.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- API_KEY
- FRED_API_KEY
- TU_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Fred Macro loads about 2.2k tokens when it runs, and up to ~8.8k if it reads all its reference files. Until then it costs about 33 tokens; SKILL.md has 669 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~33
When it runs · the whole SKILL.md, loaded when a task matches: ~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~8.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from gauss314/skills at commit 5156f81, republished under its MIT licence (© gauss314). 669 words, ~2,165 tokens.

Download SKILL.mdSave it as .claude/skills/fred-macro/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: fred-macro
description: API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).
license: MIT
metadata.category: finanzas, api, macroeconomia, federal-reserve, eeUU
metadata.language: es
metadata.source: https://fred.stlouisfed.org/docs/api/fred/

FRED Macro — Federal Reserve Economic Data API

API gratuita y oficial de la Reserva Federal de St. Louis (FRED) con 840,000+ series temporales macroeconómicas: PIB, inflación (CPI/PCE), tasas de interés, empleo, M2, VIX, treasuries, hipotecas y más.

Base URL: https://api.stlouisfed.org/fred Documentación oficial: fred.stlouisfed.org/docs/api/fred/

Autenticación

Obtener API Key (GRATIS)

Ir a: https://fred.stlouisfed.org/docs/api/api_key.html
Crear cuenta gratuita (email + contraseña)
Solicitar API key (se genera instantáneamente)
No requiere tarjeta de crédito

Usar la API Key

python

import os
API_KEY = os.getenv("FRED_API_KEY")  # Recomendado
# o directamente para pruebas:
# API_KEY = "TU_API_KEY_AQUI"

⚠️ NUNCA hardcodear la API key en código compartido/commits.

Rate Limits

Límite	Valor
Requests por minuto	120 req/min
Requests por día	Ilimitado (sin límite diario explícito)
Máx observaciones por request	100,000
Máx series por request	Depende del endpoint (generalmente 1)
Costo	Completamente GRATIS

Recomendaciones

Cachear respuestas localmente (los datos macroeconómicos cambian con poca frecuencia)
Usar observation_start y observation_end para limitar rangos
Implementar retry con backoff si se recibe HTTP 429 (Too Many Requests)
Para descargar muchas series, intercalar 0.5s entre requests

Endpoints Principales

Series y Observaciones

Endpoint	Descripción	Auth
`GET /fred/series/observations`	Valores históricos de una serie	API Key
`GET /fred/series/search`	Buscar series por texto	API Key
`GET /fred/series`	Metadatos de una serie	API Key
`GET /fred/series/categories`	Categorías de una serie	API Key
`GET /fred/series/release`	Release asociado a una serie	API Key

Categorías y Releases

Endpoint	Descripción
`GET /fred/category`	Información de una categoría
`GET /fred/category/children`	Subcategorías
`GET /fred/category/related`	Categorías relacionadas
`GET /fred/category/series`	Series en una categoría
`GET /fred/release`	Información de un release
`GET /fred/release/dates`	Fechas de un release
`GET /fred/release/series`	Series en un release

Tags (etiquetas)

Endpoint	Descripción
`GET /fred/tags`	Buscar tags
`GET /fred/related_tags`	Tags relacionados
`GET /fred/tags/series`	Series con un tag específico
`GET /fred/series/tags`	Tags de una serie

Fuentes (Sources)

Endpoint	Descripción
`GET /fred/sources`	Lista de fuentes de datos
`GET /fred/source`	Información de una fuente

Mapas de Calendario

Endpoint	Descripción
`GET /fred/series/updates`	Series actualizadas recientemente
`GET /fred/seasonal/adjustments`	Opciones de ajuste estacional

Formato de Respuesta

Por defecto devuelve XML. Se puede cambiar con &file_type=json:

json

{
  "realtime_start": "2026-06-01",
  "realtime_end": "2026-06-01",
  "observation_start": "1954-07-01",
  "observation_end": "2026-06-01",
  "units": "lin",
  "count": 864,
  "observations": [
    {
      "realtime_start": "2026-06-01",
      "realtime_end": "2026-06-01",
      "date": "1954-07-01",
      "value": "."
    },
    {
      "realtime_start": "2026-06-01",
      "realtime_end": "2026-06-01",
      "date": "1954-10-01",
      "value": "126.8"
    }
  ]
}

Nota: valores "." indican dato no disponible (N/A).

Categorías de Series

Las series FRED están organizadas en categorías (ids numéricos):

ID	Categoría	Ejemplos
0	Todas las categorías (raíz)	—
32991	Population, Employment, & Labor Markets	UNRATE, PAYEMS, NFP
32992	National Income & Product Accounts	GDP, GDPC1, GNP
32993	Consumer Price Indexes (CPI)	CPIAUCSL, CPILFESL
32994	Producer Price Indexes (PPI)	PPIACO, PPIFIS
32995	Interest Rates	FEDFUNDS, DGS10, DGS2
32996	Money, Banking, & Finance	M2SL, M1SL, TOTBKCR
32997	International Trade	BOPGSTB
33000	U.S. Regional Data	Estadísticas estatales
33001	Academic Data	Datos académicos

Ver referencia completa en references/SERIES_REFERENCE.md.

Show full SKILL.md (262 more words)Show less

Uso Rápido

1. Descargar una serie (Python)

python

import requests

API_KEY = "TU_API_KEY"
url = "https://api.stlouisfed.org/fred/series/observations"
params = {
    "series_id": "GDP",
    "api_key": API_KEY,
    "file_type": "json",
    "observation_start": "2020-01-01",
    "observation_end": "2025-12-31"
}
r = requests.get(url, params=params)
data = r.json()
for obs in data["observations"]:
    if obs["value"] != ".":
        print(obs["date"], obs["value"])

2. Buscar series por palabra clave

python

params = {
    "api_key": API_KEY,
    "file_type": "json",
    "search_text": "inflation",
    "search_type": "full_text",  # o "series_id"
    "limit": 10
}
r = requests.get("https://api.stlouisfed.org/fred/series/search", params=params)

3. Usando pandas

python

import pandas as pd
import requests

def fetch_fred(series_id, api_key, start="2020-01-01"):
    url = "https://api.stlouisfed.org/fred/series/observations"
    params = {"series_id": series_id, "api_key": api_key,
              "file_type": "json", "observation_start": start}
    r = requests.get(url, params=params)
    df = pd.DataFrame(r.json()["observations"])
    df["date"] = pd.to_datetime(df["date"])
    df["value"] = pd.to_numeric(df["value"], errors="coerce")
    return df.set_index("date")["value"]

gdp = fetch_fred("GDP", API_KEY)
cpi = fetch_fred("CPIAUCSL", API_KEY)
fedfunds = fetch_fred("FEDFUNDS", API_KEY)

Scripts Disponibles

Script	Descripción
fetch_series.py	Descarga una o más series FRED en CSV/JSON/Parquet
search_series.py	Busca series FRED por texto, categoría o tag
download_multiple.py	Descarga batches de series predefinidas por categoría

Series Esenciales (Headlines)

Serie	Descripción	Frecuencia
`GDP`	PIB Nominal (Billions $)	Trimestral
`GDPC1`	PIB Real (Billions chained $)	Trimestral
`CPIAUCSL`	IPC General (CPI All Items)	Mensual
`CPILFESL`	IPC Subyacente (Core CPI)	Mensual
`PCEPILFE`	PCE Subyacente (Core PCE)	Mensual
`FEDFUNDS`	Tasa de Fondos Federales	Diaria
`DFF`	Tasa de Fondos Federales (efectiva)	Diaria
`DGS10`	Treasury a 10 años	Diaria
`DGS2`	Treasury a 2 años	Diaria
`T10Y2Y`	Spread 10y-2y (curva invertida)	Diaria
`UNRATE`	Tasa de Desempleo	Mensual
`PAYEMS`	Nóminas no agrícolas (Nonfarm Payrolls)	Mensual
`M2SL`	M2 Money Supply	Mensual
`M1SL`	M1 Money Supply	Mensual
`VIXCLS`	VIX (volatilidad S&P 500)	Diaria
`BAA10Y`	Spread BAA - 10y (credit spread)	Diaria
`TOTALSA`	Ventas Minoristas	Mensual
`INDPRO`	Producción Industrial	Mensual
`HOUST`	Viviendas Iniciadas	Mensual
`MORTGAGE30US`	Tasa Hipoteca 30 años	Semanal

Referencia completa: references/SERIES_REFERENCE.md (100+ series documentadas).

Buenas Prácticas

Cachear datos: los datos macro no cambian frecuentemente; guardar en Parquet/CSV local
Usar file_type=json: más fácil de parsear que XML
Filtrar por fecha: usar observation_start para evitar descargar historia innecesaria
Manejar valores ".": representan datos no disponibles (NaN)
Rate limiting: 120 req/min = 1 request cada 0.5s como mínimo
API Key: usar variable de entorno FRED_API_KEY
Citar fuente: obligatorio incluir "This product uses the FRED API but is not endorsed or certified by the Federal Reserve Bank of St. Louis" en apps comerciales

Files

SKILL.md and 5 other files (scripts, references) in skills/fred-macro of gauss314/skills.

SKILL.md
references/API_REFERENCE.md
references/SERIES_REFERENCE.md
scripts/download_multiple.py
scripts/fetch_series.py
scripts/search_series.py

Open the folder on GitHubat commit 5156f81

Compare with similar skills

Fred Macro next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Fred Macro

What does Fred Macro do?

API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries). Fred Macro is an agent skill from gauss314/skills. API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).

When should I use Fred Macro?

Fred Macro fits situations like: data & Analytics work in your project.

How do I install Fred Macro in Claude Code?

Run `npx skills add gauss314/skills --skill fred-macro -a claude-code`. Or copy the skill folder (skills/fred-macro in gauss314/skills) into .claude/skills/fred-macro in your project. Claude Code loads it when a task matches its description.

How do I install Fred Macro in Codex?

Run `npx skills add gauss314/skills --skill fred-macro -a codex`. Or copy the skill folder (skills/fred-macro in gauss314/skills) into .agents/skills/fred-macro in your project. Codex loads it when a task matches its description.

Can I use Fred Macro in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gauss314/skills --skill fred-macro -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fred-macro, .gemini/skills/fred-macro, .github/skills/fred-macro and .opencode/skills/fred-macro in your project.

What does Fred Macro need to run?

Going by SKILL.md and its folder, Fred Macro needs Python for the scripts in its folder and credentials named API_KEY, FRED_API_KEY and TU_API_KEY. Our summary lists: Python 3; A credential in API_KEY; A credential in FRED_API_KEY.

Does Fred Macro access the network?

SKILL.md names 2 domains. In commands or code: api.stlouisfed.org; the agent is likely to contact it when it follows the instructions. As links in the text: fred.stlouisfed.org. This is read from the text; nothing was executed.

Is Fred Macro safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fred Macro use?

Fred Macro is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fred Macro use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.6k tokens, read only when the agent opens those files.

What are the alternatives to Fred Macro?

Skills that share tags, products or a category with Fred Macro: Exploratory Data Analysis (spacering-net/codeg, 3.8k stars), Matplotlib (zLanqing/codex-claude-academic-skills, 4.6k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars) and Chart Visualization (bytedance/deer-flow, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fred Macro?

gauss314 (a GitHub user) maintains it in gauss314/skills, which has 246 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on June 14, 2026.

Source: gauss314/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Fred Macro

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

FRED Macro — Federal Reserve Economic Data API

Autenticación

Obtener API Key (GRATIS)

Usar la API Key

Rate Limits

Recomendaciones

Endpoints Principales

Series y Observaciones

Categorías y Releases

Tags (etiquetas)

Fuentes (Sources)

Mapas de Calendario

Formato de Respuesta

Categorías de Series

Uso Rápido

1. Descargar una serie (Python)

2. Buscar series por palabra clave

3. Usando pandas

Scripts Disponibles

Series Esenciales (Headlines)

Buenas Prácticas

Files

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