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 papers-we-love-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 | papers-we-love-guide |
| description | Community-curated directory of influential CS research papers |
| metadata | {"openclaw":{"emoji":"❤️","category":"literature","subcategory":"discovery","keywords":["papers we love","CS papers","reading groups","classic papers","paper recommendations","curated list"],"source":"https://github.com/papers-we-love/papers-we-love"}} |
Papers We Love (PWL) is a community-driven repository of influential computer science research papers organized by topic, with worldwide reading groups. The repository contains direct links to PDFs and summaries for hundreds of landmark papers across distributed systems, programming languages, machine learning, security, and more. A go-to resource for discovering foundational and impactful research.
papers-we-love/
├── distributed_systems/
│ ├── README.md # Curated list with descriptions
│ ├── lamport-clocks.pdf
│ └── raft.pdf
├── machine_learning/
├── programming_languages/
├── security/
├── databases/
├── networking/
├── information_retrieval/
├── artificial_intelligence/
├── concurrency/
├── operating_systems/
└── ... (40+ categories)
| Category | Notable Papers |
|---|---|
| Distributed Systems | Paxos, Raft, MapReduce, Dynamo |
| Machine Learning | Backpropagation, Dropout, Attention, BatchNorm |
| Programming Languages | Lambda calculus, Type inference, Hindley-Milner |
| Databases | B-Trees, LSM-Trees, MVCC, Column stores |
| Security | Public-key crypto, Zero-knowledge proofs, TLS |
| Networking | TCP congestion, BGP, Software-defined networking |
| Operating Systems | Unix, Microkernel debate, Virtual memory |
| Concurrency | CSP, Actor model, Software transactional memory |
# Clone the repository
git clone https://github.com/papers-we-love/papers-we-love.git
# Browse categories
ls papers-we-love/
# Each directory has a README with curated descriptions
cat papers-we-love/distributed_systems/README.md
import os
import glob
PWL_PATH = "./papers-we-love"
# List all categories
categories = [d for d in os.listdir(PWL_PATH)
if os.path.isdir(os.path.join(PWL_PATH, d))
and not d.startswith('.')]
print(f"Categories: {len(categories)}")
# Find papers in a category
ml_papers = glob.glob(f"{PWL_PATH}/machine_learning/*.pdf")
for p in ml_papers:
print(f" {os.path.basename(p)}")
# Search across all READMEs for a topic
import re
for readme in glob.glob(f"{PWL_PATH}/*/README.md"):
with open(readme) as f:
content = f.read()
if re.search(r"consensus|paxos|raft", content, re.I):
category = os.path.basename(os.path.dirname(readme))
print(f"Found in: {category}")
# PWL chapters host monthly meetups worldwide
# Find local chapters at paperswelove.org
chapters = {
"New York": "meetup.com/papers-we-love",
"San Francisco": "meetup.com/papers-we-love-too",
"London": "meetup.com/papers-we-love-london",
"Berlin": "meetup.com/papers-we-love-berlin",
# 40+ chapters globally
}
# Video talks on YouTube
# youtube.com/@PapersWeLove — recorded presentations
# Each talk: 30-60 min paper walkthrough by practitioner
# Curate a personal reading list from PWL
essential_distributed = [
"Time, Clocks, and the Ordering of Events (Lamport, 1978)",
"The Byzantine Generals Problem (Lamport et al., 1982)",
"Impossibility of Distributed Consensus (FLP, 1985)",
"Paxos Made Simple (Lamport, 2001)",
"In Search of an Understandable Consensus Algorithm (Raft, 2014)",
"Dynamo: Amazon's Key-Value Store (DeCandia et al., 2007)",
"MapReduce: Simplified Data Processing (Dean & Ghemawat, 2004)",
]
essential_ml = [
"A Few Useful Things to Know About ML (Domingos, 2012)",
"Dropout: A Simple Way to Prevent Overfitting (Srivastava, 2014)",
"Batch Normalization (Ioffe & Szegedy, 2015)",
"Attention Is All You Need (Vaswani et al., 2017)",
"BERT: Pre-training of Deep Bidirectional Transformers (2018)",
]
## How to Contribute
1. Fork the repository
2. Add paper PDF to appropriate category directory
3. Update the category README.md with:
- Paper title and authors
- Year of publication
- Brief description (2-3 sentences)
- Why it matters
4. Submit a pull request
### README Entry Format
- :scroll: [Paper Title](link) — Brief description.
Authors (Year). *Venue*.