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Found 32 Skills
Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory. Use when the user wants continuous autonomous operation, scheduled tasks, or a self-directing agent loop.
Comprehensive guide for building production-grade LLM applications using LangChain's chains, agents, memory systems, RAG patterns, and advanced orchestration
Library of 18+ ready-to-use prompt templates and executable agents
Utiliza esta habilidad cuando el usuario quiera crear, modificar o analizar una nueva "Skill" (habilidad) para Antigravity. Proporciona instrucciones sobre estructura de carpetas, YAML y Markdown.
This skill should be used when the user asks to "develop a concept", "explore a new idea", "brainstorm a system concept", "do concept development", "create a concept document", "run Phase A", "define the problem and architecture", or mentions concept exploration, feasibility studies, concept of operations, system concept, architecture exploration, solution landscape, or NASA Phase A.
Ming Court Code —— Standardize Claude Code development processes using the institutional framework of the Ming Dynasty court. Three-level adaptive modes: Oral Edict (rapid execution), Court Debate (structured solution), Morning Court (multi-agent parallel processing).
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
Implementation guidance for creating individual agents in the Arcanea system with proper structure, capabilities, and integration.
Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.
Sharpen, refine, and optimize AI agent skills through real usage — learn from mistakes, review quality, and improve over time. Observes skill execution in the current conversation, analyzes three sources (conversation history, file diffs, user feedback), and proposes concrete improvements to the target skill's SKILL.md. Works with Claude Code and any SKILL.md-based agent framework. Use after executing any skill: `/skill-sharpen [name]` for a specific skill, or `/skill-sharpen` to auto-detect the last used. Three modes: interactive (propose one by one), observe-only (dump to LESSONS.md), review (process pending lessons).
Python port of Claude Code agent harness — tools, commands, task orchestration, and CLI entrypoint via oh-my-codex
Expertise in using open-multi-agent, a TypeScript framework for building production-grade multi-agent AI teams with task scheduling, dependency graphs, and inter-agent communication.