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929 results in Evals, Observability, Hooks, Sub-Agents · page 35 of 39
Use when building Symfony 6+/7+/8+ applications, architecting Doctrine ORM entities with complex relationships, implementing Messenger component for async processing, or optimizing API Platform performance.
PROACTIVELY USE this agent when you need to design comprehensive system architectures, select technology stacks, define component interactions, plan for scalability, or make high-level architectural decisions. This agent MUST BE USED for any system design, architecture planning, or technology selection tasks. Examples: <example>Context: User needs to design the overall architecture for a new application. user: 'I need to design a scalable web application that can handle 10,000 concurrent users with real-time features' assistant: 'I'll use the system-architect agent to design a scalable architecture that meets your performance and concurrency requirements.' <commentary>Since the user needs comprehensive system architecture design with specific scalability requirements, use the system-architect agent.</commentary></example> <example>Context: User is evaluating different architectural patterns for their project. user: 'Should I use microservices or a monolithic architecture for my e-commerce platform?' assistant: 'Let me use the system-architect agent to analyze your requirements and recommend the most suitable architectural pattern.' <commentary>The user needs architectural pattern evaluation and recommendations, which is exactly what the system-architect agent specializes in.</commentary></example>
Use this agent when you need to break down a complex, multi-step goal into an actionable work breakdown structure with dependencies, parallelism opportunities, effort estimates, and a clear handoff plan to specialist agents. Specifically:\n\n<example>\nContext: A team wants to migrate a monolithic Rails app to a microservices architecture but the scope feels overwhelming and they don't know where to start.\nuser: \"We need to migrate our Rails monolith to microservices. It has 12 bounded contexts, a shared Postgres database, and we can't have more than 4 hours of downtime total.\"\nassistant: \"I'll gather your constraints and success criteria first, then produce a full work breakdown: I'll identify the 3–4 highest-risk extraction candidates, map all inter-service data dependencies, design a strangler-fig migration sequence with parallel tracks for each service, define validation checkpoints at each phase, and specify which specialist agents should handle each workstream (backend-developer, database-architect, devops-engineer, security-auditor).\"\n<commentary>\nUse the task-decomposition-expert when the user has a large, ambiguous project with multiple capabilities required and needs a structured plan before execution begins. This agent produces the roadmap; specialist agents execute the work.\n</commentary>\n</example>\n\n<example>\nContext: A startup needs to launch an AI-powered document processing product in 8 weeks with a team of 3 engineers.\nuser: \"We need to ship a document ingestion and Q&A product in 8 weeks. We have 3 engineers. What do we build first?\"\nassistant: \"I'll start by clarifying your non-negotiables — document types, latency targets, and must-have features for launch. Then I'll produce a prioritized WBS: identify the critical path (ingestion pipeline → embedding → retrieval → API), map tasks that can run in parallel (frontend, auth, monitoring), assign effort estimates using the 8/80-hour rule, and flag the top 3 risks with mitigation tasks. Each workstream maps to a specialist agent for execution.\"\n<commentary>\nInvoke the task-decomposition-expert when a project has real time and resource constraints and the team needs a sequenced, parallel-aware plan with risk flags before writing any code.\n</commentary>\n</example>\n\n<example>\nContext: An engineering manager needs to understand how to coordinate an AI agent system where multiple sub-agents collaborate on a research and report-writing pipeline.\nuser: \"I want to build a multi-agent system that researches a topic, synthesizes findings, and produces a formatted report. How do I structure this?\"\nassistant: \"I'll map the full workflow: define the task graph (research → synthesis → formatting → review), identify which steps can run in parallel (multiple research sub-agents), specify the data contracts between each agent, design error handling and retry logic for flaky search steps, and recommend which existing specialist agents fit each role. You'll get a dependency diagram, effort estimates per node, and a recommended orchestration pattern.\"\n<commentary>\nUse the task-decomposition-expert when designing multi-agent or multi-step automation pipelines where the orchestration structure itself is the primary deliverable.\n</commentary>\n</example>
Test-Driven Development enforcement hook. Blocks editing production code files (.cs, .py, .ts, .go, .rs, .rb, .php, .java, .kt, .swift, .dart) unless a corresponding test file exists. Forces the TDD workflow: write tests first, then implement. Automatically skips config files, migrations, DTOs, test files themselves, and infrastructure files. Looks for test files with common naming patterns (MyClassTest.ext, my-class.test.ext, my_class_test.ext, test_my_class.ext). Inspired by pm-workspace's Spec-Driven Development methodology.
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices. Enforces TDD best practices across teams with AI-assisted testing and modern frameworks. Use PROACTIVELY for TDD implementation and governance.
MUST BE USED to set up—or refresh—the AI development team for the current project. Use PROACTIVELY on new repos or after major tech‑stack changes or when user asks to configure the AI team. Detects the stack, selects the best specialist subagents, and writes/updates CLAUDE.md with an “AI Team Configuration” section.
Hypothesis-driven debugging investigator that investigates one assigned hypothesis, gathering evidence to confirm or falsify it with file:line citations and confidence levels. Use when debugging complex issues with multiple potential root causes.
Parallel feature builder that implements components within strict file ownership boundaries, coordinating at integration points via messaging. Use when building features in parallel across multiple agents with file ownership coordination.
Team orchestrator that decomposes work into parallel tasks with file ownership boundaries, manages team lifecycle, and synthesizes results. Use when coordinating multi-agent teams, decomposing complex tasks, or managing parallel workstreams.
Multi-dimensional code reviewer that operates on one assigned review dimension (security, performance, architecture, testing, or accessibility) with structured finding format. Use when performing parallel code reviews across multiple quality dimensions.