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 ai-agent-papers-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 | ai-agent-papers-guide |
| description | Curated 2024-2026 AI agent research papers collection |
| metadata | {"openclaw":{"emoji":"📑","category":"domains","subcategory":"ai-ml","keywords":["AI agents","agent papers","2024 research","LLM agents","agent frameworks","survey"],"source":"https://github.com/VoltAgent/awesome-ai-agent-papers"}} |
A focused collection of AI agent research papers from 2024-2026, tracking the latest developments in LLM-based agent systems. Unlike broader collections, this focuses on recent breakthroughs — new architectures, benchmarks, multi-agent coordination, and real-world applications. Updated frequently as the field evolves rapidly.
Recent AI Agent Research
├── Agent Architectures
│ ├── Planning (o1-style reasoning, search-augmented)
│ ├── Memory (long-term, episodic, working)
│ └── Tool use (function calling, code execution)
├── Multi-Agent Systems
│ ├── Collaboration (task decomposition, debate)
│ ├── Competition (red team, adversarial)
│ └── Emergence (self-organization, culture)
├── Evaluation
│ ├── Benchmarks (SWE-bench, WebArena, GAIA)
│ ├── Safety (jailbreak, misuse, alignment)
│ └── Reliability (error recovery, hallucination)
├── Applications
│ ├── Software engineering (coding agents)
│ ├── Scientific research (lab automation)
│ ├── Web automation (browsing, form-filling)
│ └── Enterprise (workflow, data analysis)
└── Infrastructure
├── Frameworks (LangGraph, CrewAI, AutoGen)
├── Protocols (MCP, A2A, tool standards)
└── Deployment (scaling, monitoring, cost)
| Paper | Venue | Key Contribution |
|---|---|---|
| SWE-agent | ICLR 2025 | Agent interface design for SE |
| OpenHands | 2024 | Open platform for coding agents |
| AgentBench | ICLR 2024 | Multi-environment agent benchmark |
| GAIA | ICLR 2024 | General AI assistant benchmark |
| Voyager | NeurIPS 2024 | Lifelong learning in Minecraft |
| OS-Copilot | 2024 | Self-improving computer agent |
| AutoGen | 2024 | Multi-agent conversation framework |
| Agent-FLAN | ACL 2024 | Agent fine-tuning methodology |
import arxiv
from datetime import datetime, timedelta
def find_recent_agent_papers(days=14):
"""Find cutting-edge agent papers."""
queries = [
"ti:agent AND (ti:LLM OR ti:language model)",
"abs:autonomous agent AND abs:tool use AND abs:2024",
"ti:multi-agent AND abs:large language",
"abs:coding agent OR abs:software agent",
]
seen = set()
papers = []
for q in queries:
search = arxiv.Search(
query=q, max_results=15,
sort_by=arxiv.SortCriterion.SubmittedDate,
)
for r in search.results():
if r.entry_id not in seen:
seen.add(r.entry_id)
papers.append({
"title": r.title,
"date": r.published.strftime("%Y-%m-%d"),
"url": r.entry_id,
})
papers.sort(key=lambda x: x["date"], reverse=True)
for p in papers[:20]:
print(f"[{p['date']}] {p['title']}")
print(f" {p['url']}")
find_recent_agent_papers()
frameworks = {
"LangGraph": {
"paradigm": "Graph-based workflows",
"persistence": "Built-in checkpointing",
"multi_agent": "Yes",
"language": "Python/JS",
},
"CrewAI": {
"paradigm": "Role-based agents",
"persistence": "Memory module",
"multi_agent": "Yes (crew)",
"language": "Python",
},
"AutoGen": {
"paradigm": "Conversational agents",
"persistence": "Chat history",
"multi_agent": "Yes (group chat)",
"language": "Python/.NET",
},
"OpenHands": {
"paradigm": "Computer use agent",
"persistence": "Workspace state",
"multi_agent": "No",
"language": "Python",
},
}
for name, info in frameworks.items():
print(f"\n{name}:")
for k, v in info.items():
print(f" {k}: {v}")