Diego Rodrigues de Sa e Souza

Design AI agents with recommended patterns and architectures

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Check and process messages from autonomous AILANG agents. Use when starting a session, after agent handoffs, or when checking for completion notifications.

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Retain and recall work context across sessions. Use when user asks to remember something, recall previous work, or reference past discussions. Triggered by phrases like 'remember this', 'save for later', 'recall', 'what did we discuss about'.

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Send and receive messages between AI agents using AI Maestro's messaging system. Use this skill when the user asks to "send a message", "check inbox", "read messages", "notify [agent]", "tell [agent]", or any inter-agent communication.

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Discovers all Claude Code agents in the system including built-in, plugin, project, and user-level agents. Use when you need to find which agents are available, understand the agent ecosystem, or prepare agents for Actoris registration.

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Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems

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Real-time cost tracking, budget enforcement, and ROI measurement for AI agent operations. Track token usage, predict costs, enforce budget caps ($50-70/month typical), optimize model selection, cache results, measure cost-to-value. Use when tracking AI costs, preventing budget overruns, optimizing spend, measuring ROI, or ensuring cost-effective AI operations.

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Debug and troubleshoot ElevenLabs conversational AI agents and Twilio calls. Use when diagnosing agent issues, analyzing failed calls, troubleshooting audio problems, investigating conversation breakdowns, reviewing error logs, or optimizing underperforming agents. Includes transcript analysis, error diagnosis, and performance troubleshooting.

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Extend the Strategy Coach agent with new capabilities. Use when user says "add coaching phase", "new research pillar", "extend agent", "add strategic output", or asks to enhance the AI coaching methodology.

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This skill provides knowledge for generating effective Claude Code agents tailored to specific projects. It is used internally by the agent-team-creator plugin when analyzing codebases and creating specialized agent teams. Contains templates, best practices, and patterns for writing project-aware agents.

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Implement hooks for permission control and security in custom agents. Use when adding security controls, blocking dangerous operations, implementing audit trails, or designing permission governance.

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Expert LLM architect specializing in large language model architecture, deployment, and optimization. Masters LLM system design, fine-tuning strategies, and production serving with focus on building scalable, efficient, and safe LLM applications.

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Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, linked documentation.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

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Use this skill when the user asks to save, remember, recall, or organize memories. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check your notes', 'clean up memories'. Also use proactively when discovering valuable findings worth preserving.

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Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.

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Use when addressing PR review comments from Copilot, Claude, or human reviewers. Critically assesses each comment, recommends action (implement/push-back/discuss), and executes appropriately.

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Guide for adding new LLM models to Letta Code. Use when the user wants to add support for a new model, needs to know valid model handles, or wants to update the model configuration. Covers models.json configuration, CI test matrix, and handle validation.

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Add a new AI provider to agentconfig.org's comparison system. Use when integrating a new coding assistant (e.g., Cursor, Claude Desktop, GitHub Copilot alternative) with proper type system updates, implementation data, UI components, tests, and documentation.

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Create a new skill for this repository following the agentskills.io specification with proper structure and packaging

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Add a new AI model to a provider (anthropic, openai, groq, cerebras). Use when a new model is announced and needs to be supported in aico.

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Add a professional footer to README files with engagement prompts, community channels, and call-to-action for subscriptions. Use when you need to enhance your README with a footer that encourages interaction and builds community.

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Add Qdrant embedding support to v3 WordPress components for RAG chatbot. Implements component-level content chunking for searchable, structured embeddings. Use when adding embedding to new or existing v3 components.

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