# Performance Profiler > Implements intelligent performance profiler with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/performance-profiler` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/performance-profiler` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/performance-profiler/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/performance-profiler --- # 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 ```python 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 ```python 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 | --- ## 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](https://perf.wiki.kernel.org/index.php/Main) — Comprehensive guide to the Linux performance analysis toolkit - [Python cProfile Module](https://docs.python.org/3/library/profile.html#module-cProfile) — Official Python documentation for built-in code profiling - [py-spy Production Profiler](https://github.com/benfred/py-spy) — Sampling profiler for Python programs, zero-overhead production profiling - [Google Performance Tools (gperftools)](https://github.com/gperftools/gperftools) — High-performance libraries including heap and CPU profilers - [OpenTelemetry Profiling](https://opentelemetry.io/docs/specs/otel/profiling/) — Official OpenTelemetry specification for distributed profiling