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- brycewang-stanford/Auto-Empirical-Research-Skills
- 最近来源活动
- 2026年4月3日 02:07
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安装方式
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。
检查来源文件
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。
菜单
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/brycewang-stanford/Auto-Empirical-Research-Skills --skill database-comparison-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。
正在显示 SKILL.md
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
| name | database-comparison-guide |
| description | Compare major academic databases and when to use each for research |
| metadata | {"openclaw":{"emoji":"🗄️","category":"literature","subcategory":"search","keywords":["academic database search","scholarly database","search strategy","field-specific search"],"source":"wentor-research-plugins"}} |
A comprehensive reference for choosing and querying the right academic database for your research domain, including coverage details, advanced operators, and cross-database strategies.
| Database | Coverage | Disciplines | Access Model | Unique Strength |
|---|---|---|---|---|
| Web of Science | 1900-present, 21,000+ journals | Multidisciplinary | Subscription | Citation indexing, Journal Impact Factor |
| Scopus | 1970-present, 27,000+ journals | Multidisciplinary | Subscription | Largest abstract/citation DB, CiteScore |
| PubMed | 1946-present, 35M+ records | Biomedical, life sciences | Free | MeSH controlled vocabulary, clinical filters |
| IEEE Xplore | 1872-present, 6M+ docs | Engineering, CS | Subscription | Conference proceedings, standards |
| Google Scholar | Broad, undisclosed | All fields | Free | Widest coverage, full-text indexing |
| JSTOR | Historical archives | Humanities, social sciences | Subscription | Historical journal runs, primary sources |
| arXiv | 1991-present, 2.4M+ papers | Physics, CS, Math, Bio | Free | Preprints, no peer-review delay |
| SSRN | 1994-present | Social sciences, law | Free | Working papers, early-stage research |
For physics, computer science, and mathematics, combine arXiv preprints with Web of Science indexed journals:
# arXiv API query for recent ML papers
curl "http://export.arxiv.org/api/query?search_query=cat:cs.LG+AND+ti:transformer&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending"
For biomedical research, PubMed with MeSH terms provides the most precise retrieval:
# PubMed E-utilities search with MeSH
curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=%22machine+learning%22[MeSH]+AND+%22drug+discovery%22[MeSH]&retmax=50&sort=date"
TS=("deep learning" AND "drug discovery") AND PY=(2020-2025)
# TS = Topic (title + abstract + keywords)
# PY = Publication Year
# Use NEAR/x for proximity: TS=("climate" NEAR/3 "adaptation")
TITLE-ABS-KEY("deep learning" AND "drug discovery") AND PUBYEAR > 2019
# Additional operators:
# AUTHLASTNAME(smith) AND AUTHFIRST(j*)
# AFFIL("MIT" OR "Massachusetts Institute of Technology")
# REF("seminal paper title")
"deep learning"[Title/Abstract] AND "drug discovery"[Title/Abstract]
AND ("2020/01/01"[Date - Publication] : "2025/12/31"[Date - Publication])
# Use filters: Clinical Trial[pt], Review[pt], Free Full Text[Filter]
A robust literature search should query multiple databases to maximize recall: