Skill UI

Persistent file-based planning for multi-step AI-agent tasks, using task_plan.md, findings.md, and progress.md as on-disk working memory with recovery hooks and optional session catch-up.

Planificación persistente basada en archivos para tareas multipaso de agentes de IA. Mantiene task_plan.md, findings.md y progress.md en disco; los hooks del ciclo de vida inyectan contexto seleccionado de planificación del proyecto. Diseñado para investigación o trabajo que requiera 5 o más llamadas a herramientas, con recuperación automática y opciones de gate.

Provides persistent Markdown-based planning for multi-step AI agent work, keeping task_plan.md, findings.md, and progress.md on disk with lifecycle hooks and recovery.

This skill is designed for scenarios requiring complex, multi-step task decomposition. Instead of asking an Agent to perform the task directly, it guides the process of requesting a clear, actionable, sequential execution plan from a stronger Agent. The core function emphasizes that the user must execute the generated plan themselves, ensuring the output is a roadmap, not a final answer.

This skill acts as a central router, helping users determine the correct specialized skill or workflow to tackle development challenges. It guides the entire software development lifecycle, from initial idea refinement (Idea -> Ship) and prototype testing, to managing complex multi-session builds and handling incoming bugs via triage. It is essential for navigating the vast ecosystem of AI coding agents.

A skill for planning and orchestrating tasks across agents or workflows, coordinating multi-step execution. Japanese translation is currently in progress.

This toolset allows users to save and restore complete agent sessions, ensuring context continuity across multiple conversations. It captures the agent's entire state, including memory, configuration, task progress, and learning metrics. Use it when pausing work and needing to resume later with full context, or for checkpointing long-running tasks.

A comprehensive project management guide for submitting academic papers to the AAMAS conference. It details all critical milestones, including venue fit, evidence locking, abstract submission, double-blind review, rebuttal, and final camera-ready preparation. Essential for managing complex research involving game theory and multi-agent systems.

This skill automates the collection of high-quality technical news from multiple sources like Hacker News and HuggingFace. It uses a multi-agent architecture to scrape, filter, and deduplicate content, generating a curated daily Markdown report. Ideal for staying updated on cutting-edge technology and AI developments without manual searching.