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 opencontracts-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 | opencontracts-guide |
| description | Legal document annotation, versioning, and analysis platform |
| metadata | {"openclaw":{"emoji":"📋","category":"domains","subcategory":"law","keywords":["legal documents","contract annotation","document versioning","legal AI","NLP legal","MCP"],"source":"https://github.com/Open-Source-Legal/OpenContracts"}} |
OpenContracts is an open-source platform for legal document annotation, versioning, and analysis. It provides collaborative annotation tools for legal text, version tracking across document drafts, NLP-powered clause extraction, and integration with AI agents via MCP. Designed for legal researchers, law firms, and teams managing large document collections that need structured annotation and analysis.
# Docker deployment
git clone https://github.com/Open-Source-Legal/OpenContracts.git
cd OpenContracts
docker-compose up -d
# Access at http://localhost:3000
from opencontracts import Client
client = Client("http://localhost:3000")
# Upload documents
doc = client.upload(
file="contract.pdf",
metadata={
"type": "NDA",
"parties": ["Company A", "Company B"],
"date": "2025-01-15",
"jurisdiction": "Delaware",
},
)
# Version tracking
versions = client.get_versions(doc.id)
for v in versions:
print(f"v{v.number}: {v.date} — {v.changes_summary}")
# Compare versions
diff = client.compare_versions(doc.id, v1=1, v2=3)
for change in diff.changes:
print(f"[{change.type}] Section {change.section}: "
f"{change.description}")
# Create annotation project
project = client.create_project(
name="NDA Clause Analysis",
documents=[doc.id],
label_set=[
"confidentiality_scope",
"term_duration",
"exclusions",
"remedies",
"governing_law",
"dispute_resolution",
],
)
# Add annotations
client.annotate(
document_id=doc.id,
annotations=[
{
"start": 1250, "end": 1480,
"label": "confidentiality_scope",
"note": "Broad definition including derivatives",
},
{
"start": 2100, "end": 2250,
"label": "term_duration",
"note": "5-year term with auto-renewal",
},
],
)
# Automated clause extraction
clauses = client.extract_clauses(
doc.id,
clause_types=[
"indemnification",
"limitation_of_liability",
"termination",
"force_majeure",
"assignment",
],
)
for clause in clauses:
print(f"\n[{clause.type}] (confidence: {clause.confidence:.2f})")
print(f" Location: p.{clause.page}, para {clause.paragraph}")
print(f" Text: {clause.text[:100]}...")
# Risk assessment
risks = client.assess_risks(doc.id)
for risk in risks:
print(f"[{risk.severity}] {risk.clause}: {risk.description}")
{
"mcpServers": {
"opencontracts": {
"command": "npx",
"args": ["@opencontracts/mcp-server"],
"env": {
"OPENCONTRACTS_URL": "http://localhost:3000"
}
}
}
}
# Full-text search across documents
results = client.search(
query="indemnification unlimited liability",
document_types=["NDA", "MSA"],
date_range=("2024-01-01", "2025-12-31"),
)
# Analytics
stats = client.analytics(project_id=project.id)
print(f"Documents annotated: {stats.docs_complete}")
print(f"Total annotations: {stats.total_annotations}")
print(f"Inter-annotator agreement: {stats.agreement:.2f}")
print(f"Most common clause: {stats.top_clauses[0]}")