TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Federal Reserve Economic Data API for US economic indicators
Install Fred API
$ npx skills add wentorai/research-plugins --skill fred-api -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wentorai/research-plugins fred-api --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/domains/economics/fred-api .claude/skills/fred-api && rm -rf skills-srcUse ~/.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/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .claude/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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 install https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-apiType 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.
$ npx skills add wentorai/research-plugins --skill fred-api -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wentorai/research-plugins fred-api --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/domains/economics/fred-api .agents/skills/fred-api && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .agents/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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.
$ npx skills add wentorai/research-plugins --skill fred-api -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wentorai/research-plugins fred-api --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/domains/economics/fred-api .cursor/skills/fred-api && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .cursor/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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 https://github.com/wentorai/research-plugins.git --path skills/domains/economics/fred-api--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wentorai/research-plugins --skill fred-api -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wentorai/research-plugins fred-api --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/domains/economics/fred-api .gemini/skills/fred-api && rm -rf skills-srcUse ~/.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/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .gemini/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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.
$ gh skill install wentorai/research-plugins fred-apiInstalls 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).
$ npx skills add wentorai/research-plugins --skill fred-api -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/domains/economics/fred-api .github/skills/fred-api && rm -rf skills-srcUse ~/.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/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .github/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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.
$ npx skills add wentorai/research-plugins --skill fred-api -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wentorai/research-plugins fred-api --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/domains/economics/fred-api .opencode/skills/fred-api && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "fred-api" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/domains/economics/fred-api into .opencode/skills/fred-api/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fred-api", 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-api- GitHub stars
- 298
- Used in
- 1 other repo
- Token cost
- ~2k tokens
- SKILL.md length
- 589 words
- Files
- 1
- Skills in repo
- 428
- Repo updated
- First seen
- Licence
- MIT
At a glance
Federal Reserve Economic Data API for US economic indicators
- Works in 3 steps: Register for an account at… → Request an API key at… → Include the key as the api_key query…
- Tasks that involve Forecasting and time series
- SKILL.md covers Overview, Authentication, Core Endpoints and Rate Limits, plus 2 more sections
- Calls curl; reaches api.stlouisfed.org; needs FRED_API_KEY
What it does
Fred API is an agent skill from wentorai/research-plugins. Federal Reserve Economic Data API for US economic indicators
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.
When your agent uses it
- Tasks that involve Forecasting and time series
Example prompts
- “/fred-api”
Requirements
- Python 3
- A credential in FRED_API_KEY
- A credential in YOUR_KEY
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Register for an account at https://fredaccount.stlouisfed.org/login/secure/
- Request an API key at https://fredaccount.stlouisfed.org/apikeys
- Include the key as the api_key query parameter in all requests
What it can do on your machine
Read from SKILL.md and the folder at commit bf44b3c. 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
Shell commands in SKILL.md call:
curl
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.orgfredaccount.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:
FRED_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Fred API loads about 2k tokens when it runs. Until then it costs about 17 tokens; SKILL.md has 589 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 589 words, ~1,957 tokens.
Download SKILL.mdSave it as
.claude/skills/fred-api/SKILL.md (or your agent's skills folder).- name
- fred-api
- description
- Federal Reserve Economic Data API for US economic indicators
FRED API Guide
Overview
FRED (Federal Reserve Economic Data) is a database maintained by the Research Division of the Federal Reserve Bank of St. Louis. It contains over 800,000 economic time series from dozens of national and international sources, covering macroeconomic indicators, financial markets, employment, trade, monetary policy, and more.
The FRED API provides programmatic access to this extensive economic data repository. Researchers can retrieve time series observations, search for data series by keyword or category, explore release schedules, and access vintage (real-time) data for historical analysis. The data spans decades and in some cases centuries, making it invaluable for longitudinal economic research.
Economists, financial analysts, policy researchers, data scientists, and academic institutions rely on the FRED API for econometric modeling, macroeconomic forecasting, policy analysis, and teaching. It is one of the most widely used economic data APIs in academic research and is cited in thousands of peer-reviewed publications.
Authentication
Authentication requires a free API key from the Federal Reserve Bank of St. Louis.
- Register for an account at https://fredaccount.stlouisfed.org/login/secure/
- Request an API key at https://fredaccount.stlouisfed.org/apikeys
- Include the key as the
api_keyquery parameter in all requests
curl "https://api.stlouisfed.org/fred/series?series_id=GDP&api_key=YOUR_KEY&file_type=json"API keys are free and available to anyone who registers. There is no fee or approval process.
Core Endpoints
series: Retrieve Series Metadata
Get metadata about a specific economic data series, including title, frequency, units, seasonal adjustment, and date range.
- URL:
GET https://api.stlouisfed.org/fred/series - Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| series_id | string | Yes | FRED series identifier (e.g., GDP) |
| api_key | string | Yes | Your FRED API key |
| file_type | string | No | Response format: json or xml (default) |
- Example:
curl "https://api.stlouisfed.org/fred/series?series_id=UNRATE&api_key=YOUR_KEY&file_type=json"- Response: Returns
seriessarray withid,title,observation_start,observation_end,frequency,units,seasonal_adjustment,notes, andpopularityranking.
observations: Retrieve Time Series Data
Fetch actual data points (observations) for a specific economic series over a date range.
- URL:
GET https://api.stlouisfed.org/fred/series/observations - Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| series_id | string | Yes | FRED series identifier |
| api_key | string | Yes | Your FRED API key |
| observation_start | string | No | Start date in YYYY-MM-DD format |
| observation_end | string | No | End date in YYYY-MM-DD format |
| frequency | string | No | Aggregation: d, w, m, q, a |
| aggregation_method | string | No | avg, sum, eop (end of period) |
| file_type | string | No | json or xml |
- Example:
curl "https://api.stlouisfed.org/fred/series/observations?series_id=GDP&observation_start=2020-01-01&api_key=YOUR_KEY&file_type=json"- Response: Returns
observationsarray withdateandvaluefor each observation period.
Show full SKILL.md (217 more words)Show less
category: Browse Data Categories
Navigate the hierarchical FRED category system to discover available data series organized by topic.
- URL:
GET https://api.stlouisfed.org/fred/category - Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| category_id | int | Yes | Category ID (0 for root) |
| api_key | string | Yes | Your FRED API key |
| file_type | string | No | json or xml |
- Example:
curl "https://api.stlouisfed.org/fred/category/children?category_id=0&api_key=YOUR_KEY&file_type=json"- Response: Returns
categoriesarray withid,name, andparent_idfor child categories.
releases: Track Data Release Schedules
Retrieve information about data releases, which group related series that are published together.
- URL:
GET https://api.stlouisfed.org/fred/releases - Parameters:
| Parameter | Type | Required | Description |
|---|---|---|---|
| api_key | string | Yes | Your FRED API key |
| file_type | string | No | json or xml |
- Example:
curl "https://api.stlouisfed.org/fred/releases?api_key=YOUR_KEY&file_type=json"- Response: Returns
releasesarray withid,name,press_release,link, and release notes.
Rate Limits
The FRED API enforces rate limits that vary by usage. Standard limits allow approximately 120 requests per minute. Exceeding the limit returns HTTP 429 responses. For bulk data retrieval, consider using the FRED Excel add-in or downloading bulk files from https://fred.stlouisfed.org/. Academic users can contact FRED for elevated limits if needed.
Common Patterns
Retrieve and Plot GDP Data
Fetch quarterly GDP observations for macroeconomic analysis:
import requests
params = {
"series_id": "GDP",
"api_key": "YOUR_KEY",
"file_type": "json",
"observation_start": "2015-01-01"
}
resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params=params)
data = resp.json()
for obs in data["observations"]:
print(f"{obs['date']}: ${obs['value']}B")Compare Multiple Economic Indicators
Build a multi-series dataset for econometric analysis:
import requests
series_ids = ["UNRATE", "CPIAUCSL", "FEDFUNDS", "GDP"]
api_key = os.environ["FRED_API_KEY"]
for sid in series_ids:
resp = requests.get("https://api.stlouisfed.org/fred/series/observations", params={
"series_id": sid,
"api_key": api_key,
"file_type": "json",
"observation_start": "2020-01-01",
"frequency": "m"
})
obs = resp.json()["observations"]
print(f"{sid}: {len(obs)} monthly observations retrieved")Search for Series by Keyword
Discover available data series on a specific topic:
curl "https://api.stlouisfed.org/fred/series/search?search_text=consumer+price+index&api_key=YOUR_KEY&file_type=json&limit=10"References
- Official API documentation: https://fred.stlouisfed.org/docs/api/fred/
- FRED homepage: https://fred.stlouisfed.org/
- Series search: https://fred.stlouisfed.org/docs/api/fred/series_search.html
- Category tree: https://fred.stlouisfed.org/docs/api/fred/category.html
© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/domains/economics/fred-api of wentorai/research-plugins.
Open the folder on GitHubat commit bf44b3c
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Fred API 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Fred API this skillwentorai/research-plugins | 298 | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.6k | 6 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Alphaear Predictorninehills/skills | 281 | 2 repos | ~531 | Automated safety check: Pass | None |
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Timesfm Forecasting
zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
Statsmodels
zLanqing/codex-claude-academic-skills
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Find Hypertable Candidates
timescale/pg-aiguide
A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.
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Search the user's local Pensieve screenshot archive by text, app, or time range.
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Related
Categories
Questions about Fred API
What does Fred API do?
Federal Reserve Economic Data API for US economic indicators. Fred API is an agent skill from wentorai/research-plugins.
When should I use Fred API?
Fred API fits situations like: tasks that involve Forecasting and time series.
How do I install Fred API in Claude Code?
Run `npx skills add wentorai/research-plugins --skill fred-api -a claude-code`. Or copy the skill folder (skills/domains/economics/fred-api in wentorai/research-plugins) into .claude/skills/fred-api in your project. Claude Code loads it when a task matches its description.
How do I install Fred API in Codex?
Run `npx skills add wentorai/research-plugins --skill fred-api -a codex`. Or copy the skill folder (skills/domains/economics/fred-api in wentorai/research-plugins) into .agents/skills/fred-api in your project. Codex loads it when a task matches its description.
Can I use Fred API 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 wentorai/research-plugins --skill fred-api -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-api, .gemini/skills/fred-api, .github/skills/fred-api and .opencode/skills/fred-api in your project.
What does Fred API need to run?
Going by SKILL.md and its folder, Fred API needs the command-line tools its instructions call (curl) and credentials named FRED_API_KEY. Our summary lists: Python 3; A credential in FRED_API_KEY; A credential in YOUR_KEY.
Does Fred API access the network?
SKILL.md names 3 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 and fredaccount.stlouisfed.org. This is read from the text; nothing was executed.
Is Fred API 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. Review the folder before installing.
What licence does Fred API use?
Fred API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Fred API use?
About 2k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Fred API?
Skills that share tags, products or a category with Fred API: TimesFM Forecasting (google-research/timesfm, 34k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.6k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fred API?
wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 428 skills in this directory. The repository was last updated on June 19, 2026.
Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.