brycewang-stanford
brycewang-stanford/Auto-Empirical-Research-Skills
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 openaire-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 | openaire-api |
| description | Search EU-funded research outputs via the OpenAIRE Graph API |
| metadata | {"openclaw":{"emoji":"🇪🇺","category":"literature","subcategory":"search","keywords":["openaire","european research","open science","EU funding","research outputs","open access"],"source":"https://develop.openaire.eu/"}} |
OpenAIRE is the European Open Science infrastructure providing programmatic access to millions of research outputs — publications, datasets, software, and other research products — linked to EU-funded projects, organizations, and researchers. The Graph API is free, requires no authentication, and returns JSON or XML. Uniquely valuable for discovering EU/Horizon-funded research and tracing connections between research outputs, projects, and institutions.
https://api.openaire.eu
# Search by keywords
curl "https://api.openaire.eu/search/publications?keywords=climate+change+adaptation&format=json&size=10"
# Filter by open access
curl "https://api.openaire.eu/search/publications?keywords=machine+learning&openaccessonly=true&format=json"
# Filter by date
curl "https://api.openaire.eu/search/publications?keywords=CRISPR&fromDateAccepted=2023-01-01&toDateAccepted=2026-12-31&format=json"
# Filter by EU project
curl "https://api.openaire.eu/search/publications?projectID=corda__h2020::123456&format=json"
# Search by DOI
curl "https://api.openaire.eu/search/publications?doi=10.1038/s41586-023-05881-4&format=json"
# Find research datasets
curl "https://api.openaire.eu/search/datasets?keywords=genomics+sequencing&format=json&size=20"
# Open access datasets only
curl "https://api.openaire.eu/search/datasets?keywords=ocean+temperature&openaccessonly=true&format=json"
# Search EU-funded projects
curl "https://api.openaire.eu/search/projects?keywords=artificial+intelligence&funder=EC&format=json"
# Horizon 2020 projects
curl "https://api.openaire.eu/search/projects?keywords=renewable+energy&fundingStream=H2020&format=json"
# Horizon Europe projects
curl "https://api.openaire.eu/search/projects?keywords=quantum+computing&fundingStream=HE&format=json"
| Parameter | Description | Example |
|---|---|---|
keywords | Free-text search | keywords=deep+learning |
doi | Search by DOI | doi=10.1234/example |
openaccessonly | Open access filter | openaccessonly=true |
fromDateAccepted | Start date | fromDateAccepted=2023-01-01 |
toDateAccepted | End date | toDateAccepted=2026-12-31 |
funder | Funding agency | funder=EC (European Commission) |
fundingStream | Funding program | fundingStream=H2020 |
format | Response format | format=json or format=xml |
size | Results per page | size=50 (max 100) |
page | Page number | page=2 |
sortBy | Sort order | sortBy=resultdateofacceptance,descending |
import requests
BASE_URL = "https://api.openaire.eu"
def search_publications(keywords: str, open_access: bool = False,
from_date: str = None, size: int = 20) -> list:
"""Search OpenAIRE publications."""
params = {
"keywords": keywords,
"format": "json",
"size": size
}
if open_access:
params["openaccessonly"] = "true"
if from_date:
params["fromDateAccepted"] = from_date
resp = requests.get(f"{BASE_URL}/search/publications", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("response", {}).get("results", {}).get("result", []):
metadata = item.get("metadata", {}).get("oaf:entity", {}).get("oaf:result", {})
title = metadata.get("title", {})
if isinstance(title, dict):
title = title.get("$", "")
results.append({
"title": title,
"doi": metadata.get("pid", [{}])[0].get("$", "") if metadata.get() ,
: metadata.get(, {}).get(, ),
: metadata.get(, {}).get(, )[:] metadata.get()
})
results
pubs = search_publications(, open_access=, from_date=)
p pubs:
()