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 share-research-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.
正在显示 SKILL.md
基于 SOC 职业分类
| name | share-research-api |
| description | Discover open access research outputs via the SHARE notification API |
| metadata | {"openclaw":{"emoji":"📢","category":"literature","subcategory":"search","keywords":["SHARE","open access","research notification","COS","repository aggregator","preprints"],"source":"https://share.osf.io/"}} |
SHARE (SHared Access Research Ecosystem) aggregates metadata from 200+ research repositories, preprint servers, and publishers into a unified search API. Operated by the Center for Open Science, it tracks research outputs as they move through the scholarly communication cycle — from preprint to publication. Free, no authentication for search.
https://share.osf.io/api/v2
# Text search across all sources
curl "https://share.osf.io/api/v2/search/creativeworks/?q=climate+change&page[size]=20"
# Filter by type
curl "https://share.osf.io/api/v2/search/creativeworks/?q=neural+networks&filter[type]=preprint"
# Filter by source
curl "https://share.osf.io/api/v2/search/creativeworks/?q=genomics&filter[sources]=PubMed+Central"
# Filter by date
curl "https://share.osf.io/api/v2/search/creativeworks/?q=COVID-19&filter[date][gte]=2024-01-01"
# Filter by tag/subject
curl "https://share.osf.io/api/v2/search/creativeworks/?q=machine+learning&filter[tags]=deep+learning"
| Parameter | Description | Example |
|---|---|---|
q | Search query | q=CRISPR |
filter[type] | Output type | preprint, article, dataset, thesis |
filter[sources] | Source repository | PubMed Central, arXiv, Zenodo |
filter[date][gte] | From date | 2024-01-01 |
filter[date][lte] | Until date | 2026-12-31 |
filter[tags] | Tag filter | open+data |
page[size] | Results per page | page[size]=50 |
sort | Sort order | -date_updated |
| Source | Type |
|---|---|
| arXiv | Preprints |
| PubMed Central | Biomedical articles |
| Zenodo | Multi-discipline repository |
| Figshare | Data/figures |
| SSRN | Social science preprints |
| DataCite | Research data |
| Institutional repositories | Various |
import requests
BASE_URL = "https://share.osf.io/api/v2"
def search_share(query: str, output_type: str = None,
source: str = None,
from_date: str = None,
page_size: int = 20) -> list:
"""Search SHARE for research outputs."""
params = {"q": query, "page[size]": page_size}
if output_type:
params["filter[type]"] = output_type
if source:
params["filter[sources]"] = source
if from_date:
params["filter[date][gte]"] = from_date
resp = requests.get(
f"{BASE_URL}/search/creativeworks/",
params=params,
)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("data", []):
attrs = item.get("attributes", {})
results.append({
"title": attrs.get("title"),
"description": (attrs.get("description") or "")[:300],
"type": attrs.get("type"),
"date": attrs.get("date_updated", "")[:10],
"sources": attrs.get("sources", []),
: attrs.get(, []),
: attrs.get(, []),
})
results
preprints = search_share(
,
output_type=,
from_date=,
)
p preprints[:]:
()
()