domains / general / skill-catalog-mismatch / lex
The two copies are unrelated. The first copy is a legal context and contract scaffolding skill (LEX) that uses a lex CLI to search, fetch, draft, and verify legal templates across US, EU, and CA jurisdictions. The second copy is a Warden Agent Builder skill for creating and deploying LangGraph-based crypto/Web3 agents for the Warden Protocol. They share only the folder name 'lex' and the catalog context; their steps, tools, and goals do not overlap, so crowning one would hide a real alternative.
| Repository | Path |
|---|---|
| sickn33/agentic-awesome-skills | plugins/agentic-awesome-skills-claude/skills/lex/SKILL.md |
| LeoYeAI/openclaw-master-skills | skills/lex/SKILL.md |
| Role | Repository | Path |
|---|---|---|
| peer | sickn33/agentic-awesome-skills | plugins/agentic-awesome-skills-claude/skills/lex/SKILL.md |
| peer | LeoYeAI/openclaw-master-skills | skills/lex/SKILL.md |
| adapter | sickn33/agentic-awesome-skills | plugins/agentic-awesome-skills/skills/lex/SKILL.md |
| adapter | sickn33/agentic-awesome-skills | skills/lex/SKILL.md |
# Skill: lex (Catalog Mismatch) This catalog entry contains two unrelated skills that share the folder name 'lex'. ## Option A: LEX Legal Truth Engine Use when the task involves cross-jurisdictional legal context (US, EU, CA) or contract scaffolding. ### Steps 1. Identify the target jurisdiction (USA, Canada, or EU). 2. Run `lex search <query>` to find matching legal templates. 3. Run `lex get <path>` to read granular metadata and requirements. 4. Run `lex draft <description>` to generate a foundation-level document scaffold. 5. Run `lex verify` to fetch official government links and include a "Verified Sources" section in the output. 6. Use tables when comparing results across multiple jurisdictions. 7. If the jurisdiction is outside US/EU/CA scope, state that it is outside LEX coverage. ## Option B: Warden Agent Builder Use when the task involves building or deploying LangGraph-based agents for the Warden Protocol. ### Steps 1. Study the example agents in the Warden community repository to learn patterns; do not recreate them. 2. Identify a unique use case that differs from existing example agents. 3. Initialize a new agent project using `python scripts/init-agent.py <name> --template <typescript|python> --description "..."`. 4. Implement the agent's LangGraph workflow, tools, and configuration. 5. Deploy via LangSmith Deployments or custom infrastructure. 6. Ensure the agent is API-accessible and isolated (one agent per LangGraph instance).