---

name: memory-usage-analyzer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent memory usage analyzer 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: memory-usage-analyzer, memory usage analyzer, how do i memory-usage-analyzer, orchestrate memory-usage-analyzer, automate memory-usage-analyzer, agent memory-usage-analyzer 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"

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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

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

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 | |

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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 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