Skill UI

A systematic post-hoc audit skill for verifying factual, legal, and interpretive claims generated by AI. Designed for professional reliance—such as legal briefs or published advice—it classifies claims by type (empirical, legal, interpretive) and salience, searches for corroborating evidence using tiered, domain-appropriate sources (e.g., NICE guidelines, primary legislation), and scores the trustworthiness of the output before professional use.

This skill assists in designing tenant-scoped memory systems for AI agents, covering retrieval, retention, and storage strategies. It guides planning for RAG, conversation history, and vector stores while ensuring data isolation across sessions. Users learn to establish semantic and episodic records, manage permissions, and test retrieval quality before deployment.

A comprehensive workflow for building autonomous AI agents, multi-agent systems, and orchestration patterns using frameworks like CrewAI and LangGraph. Covers agent design, implementation, tool integration, memory systems, and evaluation to create robust intelligent applications.

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Ensures graceful degradation and clear failure modes. Capable of agent architecture design, tool calling, memory management, and complex task decomposition. Includes knowledge of ReAct loops, Plan-and-Execute patterns, and safety guardrails.

This toolkit provides six expert-level workflows for AI engineering, including prompt evaluation, context budget planning, RAG pipeline design, agent security audits, evaluation harness building, and product sense coaching. It transforms AI coding assistants into senior partners by offering structured methodologies, quantitative scoring, and decision frameworks. Suitable for optimizing LLM systems, designing secure agents, and planning product strategy before development.

This comprehensive workflow bundle covers the full lifecycle of AI and machine learning development. It includes LLM application design, RAG implementation, AI agent architecture, ML pipeline development, observability, and security. Suitable for building production-ready AI systems.

Design patterns for building autonomous coding agents, covering tool and function calling APIs, permission and approval systems, browser automation, and human-in-the-loop workflows inspired by Cline and OpenAI Codex.

This skill covers predictable performance degradation patterns in language models as context length increases, including lost-in-middle effects, context poisoning, and distraction. It helps diagnose agent failures and design resilient systems for large contexts.

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

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.

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.

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.