---

name: performance-profiler compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent performance profiler with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: performance-profiler, performance profiler, how do i performance-profiler, orchestrate performance-profiler, automate performance-profiler, agent performance-profiler, optimization, performance archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Performance Profiler

Orchestrates intelligent skill selection and execution for performance profiler workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def analyze_performance_metrics(
    raw_metrics: List[Dict],
    baseline_thresholds: Dict[str, float],
    target_function: str = None
) -> Dict:
    """Analyze raw profiling data to identify performance bottlenecks.
    
    Implements Law 1 (Early Exit) by validating metric structure immediately.
    Implements Law 3 (Atomic Predictability) by returning a new analysis dict.
    
    Args:
        raw_metrics: List of profiling samples (timestamp, function, duration, cpu_percent)
        baseline_thresholds: Dict of acceptable limits per metric type
        target_function: Optional filter to focus on a specific code path
        
    Returns:
        Structured analysis with bottleneck rankings, severity scores, and recommendations
    """
    if not raw_metrics:
        raise ValueError("Profiling data cannot be empty")
        
    # Law 2: Parse & validate at boundary
    validated_samples = []
    for sample in raw_metrics:
        if not all(k in sample for k in ("timestamp", "function", "duration_ms")):
            continue # Skip malformed samples gracefully
        validated_samples.append(sample)
        
    if not validated_samples:
        return {"status": "no_data", "bottlenecks": [], "recommendations": []}
        
    # Law 4: Fail fast on invalid thresholds
    for metric, limit in baseline_thresholds.items():
        if limit <= 0:
            raise ValueError(f"Invalid threshold for {metric}: must be positive")
            
    # Domain logic: Calculate impact scores and rank bottlenecks
    function_stats = {}
    for sample in validated_samples:
        func = sample["function"]
        if target_function and func != target_function:
            continue
        if func not in function_stats:
            function_stats[func] = {"total_duration": 0, "call_count": 0, "max_duration": 0}
        stats = function_stats[func]
        stats["total_duration"] += sample["duration_ms"]
        stats["call_count"] += 1
        stats["max_duration"] = max(stats["max_duration"], sample["duration_ms"])
        
    bottlenecks = []
    for func, stats in function_stats.items():
        avg_duration = stats["total_duration"] / stats["call_count"]
        impact_score = (avg_duration * stats["call_count"]) / 1000.0
        severity = "critical" if impact_score > baseline_thresholds.get("max_impact", 100) else "warning"
        bottlenecks.append({
            "function": func,
            "avg_duration_ms": round(avg_duration, 2),
            "impact_score": round(impact_score, 2),
            "severity": severity,
            "recommendation": f"Optimize {func} or reduce call frequency"
        })
        
    bottlenecks.sort(key=lambda x: x["impact_score"], reverse=True)
    
    return {
        "status": "analyzed",
        "total_samples": len(validated_samples),
        "bottlenecks": bottlenecks,
        "recommendations": [b["recommendation"] for b in bottlenecks[:3]]
    }

Pattern 2: Execution with Fallback

def run_profiling_cycle(
    target_process: str,
    profiler_config: Dict,
    fallback_strategy: str = "static_analysis"
) -> Dict:
    """Execute a performance profiling cycle with resilience patterns.
    
    Implements Law 4 (Fail Fast) by validating process state before profiling.
    Implements fallback chain for transient profiling failures.
    
    Args:
        target_process: Name or PID of the process to profile
        profiler_config: Dict containing duration, sampling_rate, and output_format
        fallback_strategy: Method to use if live profiling fails (e.g., static_analysis, cache)
        
    Returns:
        Profiling results with metadata, timing, and fallback status
    """
    # Law 1: Early exit on invalid config
    if not target_process or not profiler_config.get("duration"):
        raise ValueError("Target process and duration are required for profiling")
        
    # Law 2: Parse & validate profiler state
    if profiler_config["sampling_rate"] <= 0:
        raise ValueError("Sampling rate must be positive")
        
    attempts = 0
    max_attempts = profiler_config.get("max_attempts", 2)
    
    while attempts < max_attempts:
        try:
            # Domain logic: Initiate live profiling session
            profiler_session = _init_profiler(target_process, profiler_config)
            profiler_session.start()
            time.sleep(profiler_config["duration"])
            raw_data = profiler_session.stop()
            
            # Law 3: Return new data structure, never mutate session
            return {
                "status": "success",
                "profiler": target_process,
                "data": raw_data,
                "attempts": attempts + 1,
                "fallback_used": False,
                "timestamp": time.time()
            }
            
        except ProcessUnresponsiveError as e:
            # Law 4: Fail fast on invalid process state
            raise ProfilingError(f"Target process {target_process} is unresponsive: {e}") from e
            
        except TransientProfilerError as e:
            attempts += 1
            if attempts >= max_attempts:
                # Fallback chain: Switch to static analysis or cached metrics
                return _apply_fallback_profiling(target_process, fallback_strategy)
                
    # Should not reach here, but Law 4 compliance
    raise ProfilingError(f"Profiling failed after {max_attempts} attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Skill	Purpose
debugging-performance-optimization	Uses profiler output to identify and fix performance bottlenecks
systematic-debugging-methodology	Applies structured debugging after profiling identifies hotspots

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• Linux perf Documentation — Comprehensive guide to the Linux performance analysis toolkit
• Python cProfile Module — Official Python documentation for built-in code profiling
• py-spy Production Profiler — Sampling profiler for Python programs, zero-overhead production profiling
• Google Performance Tools (gperftools) — High-performance libraries including heap and CPU profilers
• OpenTelemetry Profiling — Official OpenTelemetry specification for distributed profiling