用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill fred-api命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
正在显示 SKILL.md
| name | fred-api |
| description | Federal Reserve Economic Data API for US economic indicators |
| metadata | {"openclaw":{"emoji":"🔍","category":"domains","subcategory":"economics","keywords":["microeconomics","macroeconomics","econometrics","development economics"],"source":"https://fred.stlouisfed.org/docs/api/fred/","requires":{"env":["FRED_API_KEY"]}}} |
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 requires a free API key from the Federal Reserve Bank of St. Louis.
api_key query parameter in all requestscurl "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.
Get metadata about a specific economic data series, including title, frequency, units, seasonal adjustment, and date range.
GET https://api.stlouisfed.org/fred/series| 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) |
curl "https://api.stlouisfed.org/fred/series?series_id=UNRATE&api_key=YOUR_KEY&file_type=json"
seriess array with id, title, observation_start, observation_end, frequency, units, seasonal_adjustment, notes, and popularity ranking.Fetch actual data points (observations) for a specific economic series over a date range.
GET https://api.stlouisfed.org/fred/series/observations| 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 |
curl "https://api.stlouisfed.org/fred/series/observations?series_id=GDP&observation_start=2020-01-01&api_key=YOUR_KEY&file_type=json"
observations array with date and value for each observation period.Navigate the hierarchical FRED category system to discover available data series organized by topic.
GET https://api.stlouisfed.org/fred/category| 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 |
curl "https://api.stlouisfed.org/fred/category/children?category_id=0&api_key=YOUR_KEY&file_type=json"
categories array with id, name, and parent_id for child categories.Retrieve information about data releases, which group related series that are published together.
GET https://api.stlouisfed.org/fred/releases| Parameter | Type | Required | Description |
|---|---|---|---|
| api_key | string | Yes | Your FRED API key |
| file_type | string | No | json or xml |
curl "https://api.stlouisfed.org/fred/releases?api_key=YOUR_KEY&file_type=json"
releases array with id, name, press_release, link, and release notes.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.
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")
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")
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"