skills/43-wentorai-research-plugins/skills/domains/law/legal-agent-skills-guide/SKILL.md
Agent skills collection for legal research and automation
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research legal-agent-skills-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
A curated collection of agent skills for legal research and automation — contract analysis, case law search, regulatory compliance checking, legal document drafting, and citation verification. Each skill provides structured capabilities that AI agents can use to assist with legal workflows. Designed for legal researchers, law firms, and compliance teams.
Legal Agent Skills
├── Research Skills
│ ├── Case law search (by jurisdiction, topic)
│ ├── Statute lookup (federal, state, international)
│ ├── Legal commentary search
│ └── Regulatory tracking
├── Analysis Skills
│ ├── Contract clause extraction
│ ├── Risk assessment
│ ├── Compliance checking
│ └── Legal argument analysis
├── Drafting Skills
│ ├── Contract drafting
│ ├── Legal memo writing
│ ├── Motion drafting
│ └── Compliance reports
├── Citation Skills
│ ├── Bluebook formatting
│ ├── Citation verification
│ ├── Shepard's-style validation
│ └── Cross-reference linking
└── Practice Management
├── Case timeline construction
├── Discovery document review
├── Deposition summary
└── Billing narrative generation
# Search case law databases
from legal_skills import CaseLawSearch
search = CaseLawSearch(jurisdictions=["federal", "california"])
cases = search.find(
query="AI liability product defect",
date_range=("2020-01-01", "2025-12-31"),
court_level="appellate",
max_results=20,
)
for case in cases:
print(f"{case.name} ({case.year})")
print(f" Court: {case.court}")
print(f" Key holding: {case.holding[:100]}...")
print(f" Citation: {case.citation}")
from legal_skills import ContractAnalyzer
analyzer = ContractAnalyzer()
# Analyze contract
analysis = analyzer.analyze("contract.pdf")
print("Risk Assessment:")
for risk in analysis.risks:
print(f" [{risk.severity}] {risk.clause}: {risk.description}")
print("\nKey Terms:")
for term in analysis.key_terms:
print(f" {term.name}: {term.value}")
print("\nMissing Clauses:")
for missing in analysis.missing_clauses:
print(f" - {missing}")
from legal_skills import BluebookFormatter
formatter = BluebookFormatter()
# Format citation
citation = formatter.format(
case_name="Brown v. Board of Education",
volume=347,
reporter="U.S.",
page=483,
year=1954,
)
print(citation)
# Brown v. Board of Education, 347 U.S. 483 (1954).
# Verify citation
valid = formatter.verify("347 U.S. 483")
print(f"Valid: {valid.is_valid}, Case: {valid.case_name}")
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".