Performance Optimization Orchestrator

--- description: "Orchestrate end-to-end application performance optimization from profiling to monitoring" argument-hint: "<application or service> [--focus latency|throughput|cost|balanced] [--depth quick-wins|comprehensive|enterprise]" --- # Performance Optimization Orchestrator ## CRITICAL BEHAVIORAL RULES You MUST follow these rules exactly. Violating any of them results in failure. 1. Execute steps in order. Do NOT skip ahead, reorder, or merge steps. 2. Write output files. Each step MUST produce its output file in .performance-optimization/ before the next step begins. Read from previous step files:do NOT rely on the context window’s memory. 3. Stop at checkpoints. When you reach a PHASE CHECKPOINT, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options. 4. Halt on failure. If any step fails (eagental error, test failure, missing dependency), STOP immediately. Display the error and ask the user how to proceed. Do NOT continue silently. 5. Use only local agents. All subagent_type references must use agents bundled with this plugin or general-purpose. No cross-plugin dependencies. 6. Never enter plan mode on your own. Do NOT use EnterPlanMode. This command IS the plan:execute it. ## Pre-flight Checks Before starting, perform these checks: ### 1. Check for an existing session Check if .performance-optimization/state.json exists: - If it exists and status is "in_progress": Read it, display the current step, and ask the user: `` Found an in-progress performance optimization session: Target: [name from state] Current step: [step from state] 1. Resume from where we left off 2. Start fresh (archives existing session) - If it exists and status is "complete": Ask whether to archive and start fresh. ### 2. Initialize state Create .performance-optimization/ directory and state.json: json { "target": "$ARGUMENTS", "status": "in_progress", "focus": "balanced", "depth": "comprehensive", "current_step": 1, "current_phase": 1, "completed_steps": [], "files_created": [], "started_at": "ISO_TIMESTAMP", "last_updated": "ISO_TIMESTAMP" } Parse $ARGUMENTS for --focus and --depth flags. Use defaults if not specified. ### 3. Parse target description Extract the target description from $ARGUMENTS (everything before the flags). This is referenced as $TARGET in prompts below. --- ## Phase 1: Performance Profiling & Baseline (Steps 1:3) ### Step 1: Comprehensive Performance Profiling Use the Task tool to launch the performance engineer: Task: subagent_type: "application-performance-performance-engineer" description: "Profile application performance for $TARGET" prompt: | Profile application performance comprehensively for: $TARGET. Generate flame graphs for CPU usage, heap dumps for memory analysis, trace I/O operations, and identify hot paths. Use APM tools like DataDog or New Relic if available. Include database query profiling, API response times, and frontend rendering metrics. Establish performance baselines for all critical user journeys. ## Deliverables 1. Performance profile with flame graphs and memory analysis 2. Bottleneck identification ranked by impact 3. Baseline metrics for critical user journeys 4. Database query profiling results 5. API response time measurements Write your complete profiling report as a single markdown document. Save the agent's output to .performance-optimization/01-profiling.md. Update state.json: set current_step to 2, add step 1 to completed_steps. ### Step 2: Observability Stack Assessment Read .performance-optimization/01-profiling.md to load profiling context. Use the Task tool: `` Task: subagent_type: "application-performance-observability-engineer" description: "Assess observability setup for $TARGET" prompt: | Assess current observability setup for: $TARGET. ## Performance Profile [Insert full contents of .performance-optimization/01-profiling.md] Review existing monitoring, distributed tracing with OpenTelemetry, log aggregation,