paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.