Skill UI

LEX is a structured truth engine designed to eliminate legal hallucinations by grounding AI agents in verified government references and legislation across 29+ jurisdictions (including US, EU, and CA). It provides deterministic context for comparing legal requirements, drafting compliant business, and employment documents, ensuring high jurisdictional accuracy.

Design and implement conversational AI assistants with context management, integration boundaries, and validation. Provides guidance, best practices, and checklists for building production-ready chatbots and AI-powered applications.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Framework-agnostic long-term project memory for AI agents. Persists architecture decisions, conventions, and context as plain Markdown files in .lore/, enabling any agent to record, recall, audit, sync, and query project knowledge.

This skill provides a marketer's decision framework rather than a tactic library. It helps users determine where first customers come from, evaluate if actions produce results, and judge marketing tests. It analyzes context like product, offer, and budget to return reasoned verdicts on whether to proceed, optimize, or stop a channel. It focuses on exchange value, test volume requirements, and kill rules to ensure data-driven decisions.

Build Model Context Protocol servers to enable LLMs to interact with external APIs and services. Supports Python (FastMCP) and Node/TypeScript (MCP SDK) for creating tools that help AI agents accomplish real-world tasks. Learn to design well-structured tools, manage context, and implement transport mechanisms like stdio or Streamable HTTP.

Comprehensive hardening guide for Model Context Protocol (MCP) servers, covering transport encryption, mTLS, authentication, authorization, input validation, and audit logging to secure AI agent integrations.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

Routes ambiguous data-observability requests to the appropriate Monte Carlo skill or workflow. Activates reactively for unclear alerts, incidents, coverage gaps, or general data quality issues, asking clarifying questions first to avoid premature tool calls.