/============================================================================/ /* 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:

/profile [path] [--mode quick|standard|deep] [--target cpu|memory|io|network|all]

Subagent Invocation:

Task("Performance Profiler", "Profile ./app with deep CPU and memory analysis", "performance-analyzer")

MCP Tool:

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:

{
  "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] <promise>WHEN_PROFILING_PERFORMANCE_USE_PERFORMANCE_PROFILER_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]