Skill UI

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.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

A persistent file planning system for multi-step AI agent work. It saves task plans, findings, and progress to disk, acting as disk-based working memory. Includes lifecycle hooks for context injection and session recovery. Suitable for research or tasks requiring 5+ tool calls. No network upload path.

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

Continuous file-based planning system for multi-step AI agents. Maintains task_plan.md, findings.md, and progress.md files on disk to track project progress. Injects planning context into lifecycle hooks and supports session recovery. Designed for complex research or development tasks requiring 5+ tool calls.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

This skill detects and recovers text accidentally typed using the Taiwanese Bopomofo keyboard while the input method was stuck in English mode. It maps garbled keystrokes like su3cl3a8 back to Traditional Chinese characters using a decoder script and context analysis. It is designed for Taiwanese developers who forget to switch input methods when prompting AI, ensuring requests are understood without retyping. It excludes technical strings like code or URLs.