Diego Rodrigues de Sa e Souza

Creates new agent skills following modern best practices with proper structure and documentation. Use when asked to build a new skill, organize skill resources, design skill descriptions, or validate skill structure for portability across Copilot platforms.

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Skillの割当表（agent -> skills）を作るときのルール集。skill-assigner が mapping を更新するときに参照する。

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Imported skill agent from langchain

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Intelligent financial management skill for Claude Code that provides comprehensive PocketSmith API integration with AI-powered analysis, transaction categorization, rule management, tax intelligence, and scenario planning. Use when working with PocketSmith data for (1) Transaction categorization and rule management, (2) Financial analysis and reporting, (3) Australian tax compliance (ATO) and deduction tracking, (4) Scenario analysis and forecasting, (5) PocketSmith setup health checks, (6) Budget optimization and spending insights.

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Designs the cognitive blueprint of an agent before code generation.

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Guide creation of focused single-purpose agents following the One Agent One Prompt One Purpose principle. Use when designing new agents, refactoring general agents into specialists, or optimizing agent context for a single task.

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Designs multi-agent systems with coordinated agent swarms, task distribution, inter-agent communication, and emergent collective behavior.

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Create tailored resume from job + source resume. Orchestrates the rewriting of bullet points to emphasize relevant experience without fabrication.

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Conduct multi-agent task orchestration and workflow coordination.

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Delegate tasks across multi-agent architectures with proper context preservation.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa

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Reference for configuring tool permissions when launching Claude Code agents. Use when setting up --allowedTools flags, restricting file access, or configuring agent permissions.

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Optimize ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop

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Validate agent YAML frontmatter and configuration. Use before committing agent changes or in CI.

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Create and manage AI agent sessions with multiple backends (SDK, Claude CLI, Codex, Cursor). Also supports multi-agent workflows with shared context, @mention coordination, and collaborative voting. Use for "start agent session", "create worker", "run agent", "multi-agent workflow", "agent collaboration", "test with tools", or when orchestrating AI conversations programmatically.

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AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.

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Guide plan → instrument → execute → validate with explicit checkpoints and questions. Use for ambiguous tasks or when enforcing a consistent agent workflow.

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Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates building-agents-* and testing-agent skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.

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LLM prompt management and evaluation platform. Version prompts, run A/B tests, evaluate with metrics, and deploy with confidence using Agenta's self-hosted solution.

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Set up OpenRouter as your LLM provider. Guides through account creation, API key setup, config, and making it the default model. Use when a user wants to use OpenRouter models like Claude Sonnet 4.5.

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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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