# Memory Usage Analyzer > Implements intelligent memory usage analyzer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/memory-usage-analyzer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/memory-usage-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/memory-usage-analyzer/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/memory-usage-analyzer --- # Memory Usage Analyzer Orchestrates intelligent skill selection and execution for memory usage analyzer 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_memory_snapshot( system_metrics: Dict[str, Any], process_list: List[Dict[str, Any]], threshold_pct: float = 85.0 ) -> Dict[str, Any]: """Analyze system memory state and identify top consumers. Parses raw memory metrics and process data to calculate: - Overall system utilization percentage - Top N memory-consuming processes - Anomaly detection for sudden usage spikes Args: system_metrics: Dict containing total, available, used, buffers, cached memory process_list: List of process dicts with 'pid', 'name', 'rss', 'vms' threshold_pct: Alert threshold for memory utilization Returns: Structured analysis dict with metrics, top consumers, and alerts """ total_mem = system_metrics.get("total", 0) used_mem = system_metrics.get("used", 0) available_mem = system_metrics.get("available", 0) if total_mem == 0: raise ValueError("Invalid system metrics: total memory cannot be zero") utilization_pct = (used_mem / total_mem) * 100 cache_pct = (system_metrics.get("cached", 0) / total_mem) * 100 # Identify top consumers by RSS sorted_processes = sorted(process_list, key=lambda p: p.get("rss", 0), reverse=True) top_consumers = [ {"pid": p["pid"], "name": p["name"], "rss_mb": round(p["rss"] / 1024 / 1024, 2)} for p in sorted_processes[:5] ] # Anomaly detection: flag if utilization exceeds threshold or cache is unusually low alerts = [] if utilization_pct > threshold_pct: alerts.append(f"CRITICAL: Memory utilization at {utilization_pct:.1f}% exceeds {threshold_pct}% threshold") if cache_pct < 5.0 and available_mem < (total_mem * 0.1): alerts.append("WARNING: Low cache ratio with critically low available memory") return { "utilization_pct": round(utilization_pct, 2), "cache_pct": round(cache_pct, 2), "available_mb": round(available_mem / 1024 / 1024, 2), "top_consumers": top_consumers, "alerts": alerts, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def generate_memory_report( analysis: Dict[str, Any], historical_baseline: Dict[str, float], retention_policy: str = "standard" ) -> Dict[str, Any]: """Generate actionable memory usage report with recommendations. Compares current analysis against historical baselines to detect trends, calculates memory pressure index, and formulates remediation steps. Args: analysis: Output from analyze_memory_snapshot historical_baseline: Dict with avg_utilization, avg_available_mb, peak_utilization retention_policy: Data retention tier affecting report depth Returns: Formatted report dict with trend analysis, pressure index, and recommendations """ current_util = analysis["utilization_pct"] baseline_avg = historical_baseline.get("avg_utilization", 50.0) baseline_peak = historical_baseline.get("peak_utilization", 80.0) # Calculate memory pressure index (0-100 scale) pressure_delta = current_util - baseline_avg pressure_index = min(100, max(0, 50 + (pressure_delta * 2))) recommendations = [] if current_util > baseline_peak: recommendations.append("Scale horizontally or optimize top consumers immediately") elif pressure_index > 75: recommendations.append("Review application memory pools and garbage collection settings") else: recommendations.append("Memory usage within normal operational parameters") if retention_policy == "detailed": recommendations.extend([ "Capture heap dump for deep analysis", "Enable memory profiling for top 3 processes" ]) return { "report_id": uuid.uuid4().hex[:8], "pressure_index": pressure_index, "trend": "stable" if abs(pressure_delta) < 5 else ("increasing" if pressure_delta > 0 else "decreasing"), "recommendations": recommendations, "analysis_snapshot": analysis, "generated_at": datetime.now().isoformat() } ``` ### 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 | | --- --- ## 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 skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Python Memory Profiling (tracemalloc)]() - [Valgrind Memory Debugging Tool]() - [AddressSanitizer for Memory Errors]() - [glibc malloc Stats (malloc_stats)]() - [Memory Leak Detection Best Practices]()