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Multi-source financial data reader: FRED, World Bank, OECD, Eurostat, St. Louis Fed, Yahoo, Google, and more. Standard interface for economic and financial time series data ingestion.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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/mkurman/zorai --skill pandas-datareader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。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | pandas-datareader |
| description | Multi-source financial data reader: FRED, World Bank, OECD, Eurostat, St. Louis Fed, Yahoo, Google, and more. Standard interface for economic and financial time series data ingestion. |
| tags | ["financial-data","economic-data","fred","world-bank","time-series","python","zorai"] |
pandas-datareader provides a unified interface for reading economic and financial time series from multiple sources: FRED (Federal Reserve), World Bank, OECD, Eurostat, Yahoo Finance, and St. Louis Fed. Standard tool for macroeconomic data ingestion.
uv pip install pandas-datareader
import pandas_datareader.data as web
import datetime
start = datetime.datetime(2020, 1, 1)
gdp = web.DataReader("GDP", "fred", start)
unemp = web.DataReader("UNRATE", "fred", start)
cpi = web.DataReader("CPIAUCSL", "fred", start)
fedfunds = web.DataReader("FEDFUNDS", "fred", start)
ten_year = web.DataReader("DGS10", "fred", start)
import pandas as pd
combined = pd.DataFrame({"GDP": gdp["GDP"], "CPI": cpi["CPIAUCSL"], "FedFunds": fedfunds["FEDFUNDS"]})
print(combined.tail())
gdp_pc = web.DataReader("NY.GDP.PCAP.CD", "worldbank", start=2015)