Skill UI

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

A comprehensive guide and implementation patterns for constructing sophisticated autonomous AI agents. It details the core 'Observe-Think-Decide-Act' agent loop, multi-model orchestration strategies, and robust tool design principles (like file I/O and function calling). Ideal for building complex, highly capable agents that require advanced planning, reasoning, and external tool interaction.

Design production-grade multi-agent workflows by selecting patterns like sequential or parallel, defining handoff contracts, and managing context/cost. Ideal for architecting complex LLM pipelines with error recovery and validation loops.

A robust framework for building complex, long-horizon AI agents using LangGraph. It addresses common pitfalls in advanced agent design, such as unbounded state growth (Virtual FS cleanup) and subagent persona leakage (explicit message overriding). Ideal for tasks requiring multi-step planning, task delegation, and self-correction through reflection loops.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

Design and review goal-oriented agent loops to prevent failure modes like spinning or Goodhart-gaming. Covers defining machine-decidable goals, selecting loop types (servo/regulator), and implementing plan/build/judge skeletons. Essential for building stable autonomous agents.

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.

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.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.