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Found 163 Skills
Comprehensive verification with parallel test agents. Use when verifying implementations or validating changes.
Create or extend a nao project's RULES.md. Owns the RULES.md template. Use when the user wants to generate the initial RULES.md from synced metadata (called by setup-context), or improve their existing RULES.md. Do not use for first-time scope setup (use setup-context) or for diagnosing existing problems (use audit-context).
Expert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks.
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
LangGraph state-machine design and debugging for `StateGraph`, node/edge routing, checkpoints, `interrupt`, and HITL flows. Use when building or troubleshooting graph-based agents with conditional edges and thread state.
Orchestration mode for implementation tasks. Manages the plan → code → review loop. Use /orchestrate <requirements> or let /implement invoke it.
This skill should be used when the user asks to "wrap up session", "end session", "session wrap", "/wrap", "document learnings", "what should I commit", or wants to analyze completed work before ending a coding session.
Universal Assistant — Automatically analyzes scenarios, takes inventory of ECC resources, intelligently routes to the optimal agent pipeline, and completes complex workflows with one click.
Resolve all pending CLI todos using parallel processing
Protocolo de Inteligência Pré-Tarefa — ativa TODOS os agentes relevantes do ecossistema ANTES de executar qualquer tarefa solicitada pelo usuário. Enriquece o contexto com análise paralela...
Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
Systematically fix all failing tests after business logic changes or refactoring