# When Profiling Performance Use Performance Profiler > When Profiling Performance Use Performance Profiler - Skill: `dnyoussef/when-profiling-performance-use-performance-profiler` (Agent Skill, multi-file: 8 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-profiling-performance-use-performance-profiler` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-profiling-performance-use-performance-profiler/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-profiling-performance-use-performance-profiler --- /*============================================================================*/ /* WHEN-PROFILING-PERFORMANCE-USE-PERFORMANCE-PROFILER SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: when-profiling-performance-use-performance-profiler version: 1.0.0 description: | [assert|neutral] Comprehensive performance profiling, bottleneck detection, and optimization system [ground:given] [conf:0.95] [state:confirmed] category: performance tags: - performance - profiling - optimization - benchmarking - mece author: Claude Code cognitive_frame: primary: evidential goal_analysis: first_order: "Execute when-profiling-performance-use-performance-profiler workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic performance processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "when-profiling-performance-use-performance-profiler", category: "performance", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["when-profiling-performance-use-performance-profiler", "performance", "workflow"], context: "user needs when-profiling-performance-use-performance-profiler capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Performance Profiler Skill ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Overview **When profiling performance, use performance-profiler** to measure, analyze, and optimize application performance across CPU, memory, I/O, and network dimensions. ## MECE Breakdown ### Mutually Exclusive Components: 1. **Baseline Phase**: Establish current performance metrics 2. **Detection Phase**: Identify bottlenecks and hot paths 3. **Analysis Phase**: Root cause analysis and impact assessment 4. **Optimization Phase**: Generate and prioritize recommendations 5. **Implementation Phase**: Apply optimizations with agent assistance 6. **Validation Phase**: Benchmark improvements and verify gains ### Collectively Exhaustive Coverage: - **CPU Profiling**: Function execution time, hot paths, call graphs - **Memory Profiling**: Heap usage, allocations, leaks, garbage collection - **I/O Profiling**: File system, database, network latency - **Network Profiling**: Request timing, bandwidth, connection pooling - **Concurrency**: Thread utilization, lock contention, async operations - **Algorithm Analysis**: Time complexity, space complexity - **Cache Analysis**: Hit rates, cache misses, invalidation patterns - **Database**: Query performance, N+1 problems, index usage ## Features ### Core Capabilities: - Multi-dimensional performance profiling (CPU, memory, I/O, network) - Automated bottleneck detection with prioritization - Real-time profiling and historical analysis - Flame graph generation for visual analysis - Memory leak detection and heap snapshots - Database query optimization - Algorithmic complexity analysis - A/B comparison of before/after optimizations - Production-safe profiling with minimal overhead - Integration with APM tools (New Relic, DataDog, etc.) ### Profiling Modes: - **Quick Scan**: 30-second lightweight profiling - **Standard**: 5-minute comprehensive analysis - **Deep**: 30-minute detailed investigation - **Continuous**: Long-running production monitoring - **Stress Test**: Load-based profiling under high traffic ## Usage ### Slash Command: ```bash /profile [path] [--mode quick|standard|deep] [--target cpu|memory|io|network|all] ``` ### Subagent Invocation: ```javascript Task("Performance Profiler", "Profile ./app with deep CPU and memory analysis", "performance-analyzer") ``` ### MCP Tool: ```javascript mcp__performance-profiler__analyze({ project_path: "./app", profiling_mode: "standard", targets: ["cpu", "memory", "io"], generate_optimizations: true }) ``` ## Architecture ### Phase 1: Baseline Measurement 1. Establish current performance metrics 2. Define performance budgets 3. Set up monitoring infrastructure 4. Capture baseline snapshots ### Phase 2: Bottleneck Detection 1. CPU profiling (sampling or instrumentation) 2. Memory profiling (heap analysis) 3. I/O profiling (syscall tracing) 4. Network profiling (packet analysis) 5. Database profiling (query logs) ### Phase 3: Root Cause Analysis 1. Correlate metrics across dimensions 2. Identify causal relationships 3. Calculate performance impact 4. Prioritize issues by severity ### Phase 4: Optimization Generation 1. Algorithmic improvements 2. Caching strategies 3. Parallelization opportunities 4. Database query optimization 5. Memory optimization 6. Network optimization ### Phase 5: Implementation 1. Generate optimized code with coder agent 2. Apply database optimizations 3. Configure caching layers 4. Implement parallelization ### Phase 6: Validation 1. Run benchmark suite 2. Compare before/after metrics 3. Verify no regressions 4. Generate performance report ## Output Formats ### Performance Report: ```json { "project": "my-app", "profiling_mode": "standard", "duration_seconds": 300, "baseline": { "requests_per_second": 1247, "avg_response_time_ms": 123, "p95_response_time_ms": 456, "p99_response_time_ms": 789, "cpu_usage_percent": 67, "memory_usage_mb": 512, "error_ /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/performance/when-profiling-performance-use-performance-profiler/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "when-profiling-performance-use-performance-profiler-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] WHEN_PROFILING_PERFORMANCE_USE_PERFORMANCE_PROFILER_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]