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 doi-resolution-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 | doi-resolution-guide |
| description | DOI content negotiation and metadata retrieval techniques |
| metadata | {"openclaw":{"emoji":"🔗","category":"literature","subcategory":"metadata","keywords":["DOI resolution","digital object identifier","citation statistics"],"source":"wentor-research-plugins"}} |
Master DOI content negotiation to programmatically retrieve structured metadata, citation data, and formatted references from any Digital Object Identifier.
Every DOI (e.g., 10.1038/s41586-021-03819-2) resolves to a landing page by default. However, the DOI system supports HTTP content negotiation: by sending different Accept headers, you can retrieve structured metadata in various formats instead of an HTML page.
The DOI resolver endpoint is https://doi.org/{doi} or equivalently https://dx.doi.org/{doi}.
| Accept Header | Format | Use Case |
|---|---|---|
application/vnd.citationstyles.csl+json | CSL-JSON | Programmatic metadata extraction |
text/x-bibliography; style=apa | Formatted citation | Ready-to-paste APA reference |
text/x-bibliography; style=bibtex | BibTeX | LaTeX bibliography import |
application/x-bibtex | BibTeX (alt) | LaTeX bibliography import |
application/rdf+xml | RDF/XML | Linked data applications |
text/turtle | Turtle RDF | Linked data applications |
application/vnd.crossref.unixref+xml | CrossRef Unixref | Full CrossRef metadata |
curl -LH "Accept: application/vnd.citationstyles.csl+json" \
https://doi.org/10.1038/s41586-021-03819-2
import requests
doi = "10.1038/s41586-021-03819-2"
headers = {"Accept": "application/vnd.citationstyles.csl+json"}
response = requests.get(f"https://doi.org/{doi}", headers=headers, allow_redirects=True)
metadata = response.json()
print(f"Title: {metadata['title']}")
print(f"Authors: {', '.join(a.get('family', '') for a in metadata.get('author', []))}")
print(f"Journal: {metadata.get('container-title', 'N/A')}")
print(f"Year: {metadata.get('published', {}).get('date-parts', [[None]])[0][0]}")
print(f"Type: {metadata.get('type')}")
# APA format
curl -LH "Accept: text/x-bibliography; style=apa" \
https://doi.org/10.1038/s41586-021-03819-2
# Chicago format
curl -LH "Accept: text/x-bibliography; style=chicago-author-date" \
https://doi.org/10.1038/s41586-021-03819-2
# Harvard format
curl -LH "Accept: text/x-bibliography; style=harvard-cite-them-right" \
https://doi.org/10.1038/s41586-021-03819-2
curl -LH "Accept: application/x-bibtex" \
https://doi.org/10.1038/s41586-021-03819-2
Output:
@article{Jumper_2021,
title={Highly accurate protein structure prediction with AlphaFold},
volume={596},
DOI={10.1038/s41586-021-03819-2},
journal={Nature},
author={Jumper, John and Evans, Richard and ...},
year={2021},
pages={583--589}
}
The CrossRef API provides richer metadata and supports batch queries without content negotiation.
import requests
doi = "10.1038/s41586-021-03819-2"
response = requests.get(
f"https://api.crossref.org/works/{doi}",
headers={"User-Agent": "ResearchClaw/1.0 (mailto:you@university.edu)"}
)
work = response.json()["message"]
print(f"Title: {work['title'][0]}")
print(f"Publisher: {work['publisher']}")
print(f"Citation count: {work.get('is-referenced-by-count', 0)}")
print(f"Reference count: {work.get('references-count', 0)}")
print(f"License: {work.get('license', [{}])[0].get('URL', 'N/A')}")
dois = [
"10.1038/s41586-021-03819-2",
"10.1126/science.abj8754",
"10.1016/j.cell.2021.06.025"
]
results = []
for doi in dois:
resp = requests.get(
f"https://api.crossref.org/works/{doi}",
headers={"User-Agent": "ResearchClaw/1.0 (mailto:you@university.edu)"}
)
if resp.status_code == 200:
results.append(resp.json()["message"])
else:
print(f"Failed to resolve: {doi}")
import re
def normalize_doi(raw_input):
"""Extract and normalize a DOI from various input formats."""
# Match DOI pattern: 10.XXXX/...
match = re.search(r'(10\.\d{4,9}/[^\s]+)', raw_input)
if match:
doi = match.group(1)
# Remove trailing punctuation
doi = doi.rstrip('.,;:)')
return doi.lower()
return None
# Examples
normalize_doi("https://doi.org/10.1038/s41586-021-03819-2") # 10.1038/s41586-021-03819-2
normalize_doi("DOI: 10.1038/s41586-021-03819-2.") # 10.1038/s41586-021-03819-2
normalize_doi("See paper at doi.org/10.1038/s41586-021-03819-2 for details") # works too
User-Agent header that includes a mailto: contact. This is their "polite pool" with higher rate limits.allow_redirects=True (or -L in curl) as DOIs redirect through the resolver.