Design e Frontend

Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-patterns.

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagent_type and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinking_blocks, turn_c

Operar e manter o servidor MCP local para este projeto. Utilize ao criar configurações de host MCP, solucionar problemas de conectividade de ferramentas, modificar domínios de ferramentas ou ajustar sinalizadores de política de segurança.

Analyze workflow patterns using the Agent Monitor's workflow intelligence API — orchestration DAGs, tool flow transitions, subagent effectiveness, model delegation patterns, error propagation by depth, concurrency lanes, compaction impact, and agent co-occurrence. Produces prioritized optimization recommendations with quantified impact.

Compile a weekly productivity report using Agent Monitor data — daily_sessions and daily_events trends, per-session costs from pricing engine, token volumes (input/output/cache_read/cache_write + baselines), tool usage top 20, session completion rates by status, and workflow intelligence metrics.

Analyzes a codebase's modularity imbalances using the Balanced Coupling model and produces a review of design issues. Use when reviewing existing code for coupling problems, assessing architecture quality, identifying distributed monolith risks, or finding areas where changes are unexpectedly expensive.

Produces modularity review documents in both Markdown and HTML formats. Use when writing the final review output from a modularity analysis.

O modelo de Acoplamento Balanceado é uma abordagem de design de software para criar arquiteturas modulares, avaliar o acoplamento entre componentes e decidir sobre a divisão ou fusão de módulos. Ele auxilia na avaliação de padrões de integração, na classificação do acoplamento e na compreensão dos trade-offs entre coesão e acoplamento.

Projeta arquiteturas modulares de alto nível a partir de requisitos funcionais e produz documentos de design para cada módulo. Utilize ao projetar um novo sistema, criar documentação de arquitetura ou produzir especificações de design em nível de módulo com contratos de integração e especificações de teste.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Build LiveView: async data (assign_async), PubSub (check connected?), phx-change events, form components/modals/uploads, streams for lists, live_patch. Use when handling interactions, debugging events, or tracking Presence.

Implement small Phoenix changes without planning — add validations, update routes, fix components, create migrations. Use for single-file edits under 50 lines.