Skill UI

Learn to design Jev-assisted workflows using collected use cases, references, and examples. Adapt patterns for classification, scoring, or candidate selection, including batch judgments and agent checkpoints.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.

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.

Loki Mode is a multi-agent autonomous startup system designed to execute workflows from PRD to production without human intervention. It integrates research-enhanced AI models including OpenAI SDK, DeepMind, Anthropic, and AWS Bedrock. This skill ensures zero human involvement in the deployment process while maintaining strict constraints on dependencies and response times.

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.

Authorized macOS and Mach-O reverse engineering: inspect code signatures, entitlements and Hardened Runtime, recover Objective-C/Swift structures, and triage Apple-platform malware with otool, Hopper, Ghidra, Frida and lldb.

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.

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.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

Design and optimize production-grade multi-agent systems using LangGraph, LangChain, and DeepAgents. Provides structured workflows for creating supervisor, planner, researcher, coder, and validator agents with memory and tool-calling pipelines.

Simulate a structured peer-review process using multiple specialized agents to validate designs, surface hidden assumptions, and identify failure modes before implementation.