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 world-bank-data-guide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 | world-bank-data-guide |
| description | Access World Bank development indicators and country statistics |
| metadata | {"openclaw":{"emoji":"🌍","category":"domains","subcategory":"economics","keywords":["world-bank","development","indicators","poverty","economics","statistics"],"source":"https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api"}} |
The World Bank Data API provides free access to one of the most comprehensive collections of global development data available. The Indicators API covers over 16,000 development indicators across 217 countries and territories, with time series data spanning several decades. Topics include poverty and inequality, health outcomes, education statistics, infrastructure, trade, environmental metrics, and governance indicators.
This API is a cornerstone resource for researchers in development economics, public health, education policy, environmental science, and political science. The World Bank's data is widely cited in academic publications and policy documents, making it an authoritative source for cross-country comparative research.
The API is entirely free, requires no authentication, and returns data in JSON, XML, or other formats. The v2 API supports pagination, filtering by date ranges and income levels, and provides metadata about indicators and data sources.
No authentication is required. The World Bank Data API is free and open.
# No API key needed
curl "https://api.worldbank.org/v2/country/US/indicator/NY.GDP.MKTP.CD?format=json&date=2020:2024"
GET https://api.worldbank.org/v2/country/{country_code}/indicator/{indicator_code}?format=json
Parameters:
country_code: ISO 2-letter or 3-letter code; use all for all countriesindicator_code: World Bank indicator codeformat: json or xml (default xml)date: Year range (e.g., 2015:2024)per_page: Results per page (default 50, max 32500)page: Page numberCommon Indicators:
NY.GDP.MKTP.CD: GDP (current US$)NY.GDP.PCAP.CD: GDP per capita (current US$)SP.POP.TOTL: Total populationSI.POV.DDAY: Poverty headcount ratio at $2.15/daySE.ADT.LITR.ZS: Adult literacy rateSH.XPD.CHEX.GD.ZS: Current health expenditure (% of GDP)EN.ATM.CO2E.PC: CO2 emissions (metric tons per capita)Example: GDP per capita for BRICS nations:
curl -s "https://api.worldbank.org/v2/country/BR;RU;IN;CN;ZA/indicator/NY.GDP.PCAP.CD?format=json&date=2019:2023&per_page=100" \
| python3 -m json.tool
curl -s "https://api.worldbank.org/v2/indicator?format=json&per_page=50" \
| python3 -m json.tool
curl -s "https://api.worldbank.org/v2/topic/8/indicator?format=json&per_page=20" \
| python3 -m json.tool
Topics include: 1=Agriculture, 3=Economy, 4=Education, 6=Environment, 8=Health, 11=Poverty, etc.
curl -s "https://api.worldbank.org/v2/country/CN?format=json" \
| python3 -m json.tool
import requests
import time
BASE_URL = "https://api.worldbank.org/v2"
def get_indicator(indicator_code, countries="all", date_range="2015:2023", per_page=500):
"""Fetch World Bank indicator data."""
url = f"{BASE_URL}/country/{countries}/indicator/{indicator_code}"
params = {
"format": "json",
"date": date_range,
"per_page": per_page
}
resp = requests.get(url, params=params)
resp.raise_for_status()
data = resp.json()
if len(data) < 2:
return []
return data[1]
# Compare health expenditure vs life expectancy
health_exp = get_indicator("SH.XPD.CHEX.GD.ZS", countries="US;GB;DE;JP;BR;IN", date_range="2020")
for record in health_exp:
if record["value"] is not None:
country = record["country"]["value"]
year = record["date"]
value = record["value"]
print(f"{country} ({year}): {value:.1f}% of GDP on health")
import requests
import csv
def build_panel(indicators, countries, years):
"""Build a panel dataset from multiple World Bank indicators."""
panel = {}
for ind_code, ind_name in indicators.items():
url = f"https://api.worldbank.org/v2/country/{countries}/indicator/{ind_code}"
params = {"format": "json", "date": years, "per_page": 5000}
resp = requests.get(url, params=params)
data = resp.json()
if len(data) < 2:
continue
for record in data[1]:
key = (record["country"]["id"], record["date"])
if key not in panel:
panel[key] = {"country": record["country"]["value"], "year": record["date"]}
panel[key][ind_name] = record["value"]
return list(panel.values())
indicators = {
"NY.GDP.PCAP.CD": "gdp_per_capita",
"SP.POP.TOTL": "population",
"SE.ADT.LITR.ZS": "literacy_rate"
}
data = build_panel(indicators, "BR;IN;NG;ID", "2018:2023")
for row data[:]:
(row)
Development Panel Regressions: Combine multiple indicators across countries and years to construct panel datasets for econometric analysis. Study relationships between education spending, health outcomes, and economic growth.
Poverty and Inequality Analysis: Track poverty headcount ratios, Gini coefficients, and income share data across countries and over time to study the effectiveness of development interventions.
Environmental-Economic Nexus: Combine CO2 emissions, renewable energy, and GDP data to study the Environmental Kuznets Curve or the decoupling of economic growth from environmental degradation.
Policy Evaluation: Compare indicator trajectories before and after major policy reforms across treated and comparison countries using difference-in-differences or synthetic control methods.
per_page=32500 for single-page retrieval of most queriesformat=json as the default is XMLEUU (EU), WLD (World), LIC (Low Income) for pre-computed aggregatesMRV=1 to get the most recent value for each country when the latest year varies