---

name: hot-path-detector compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent hot path detector 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: hot-path-detector, hot path detector, how do i hot-path-detector, orchestrate hot-path-detector, automate hot-path-detector, agent hot-path-detector 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"

---

Hot Path Detector

Orchestrates intelligent skill selection and execution for hot path detector 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 detect_hot_path_and_select_defense(
    request_metrics: Dict[str, Any],
    available_defenses: List[Dict],
    latency_threshold_ms: float = 200.0
) -> Optional[Dict]:
    """Identify hot paths in the request pipeline and select optimal defensive routing.
    
    Applies Law 1 (Early Exit) by rejecting malformed metrics immediately.
    Uses Law 2 (Immutable State) to create fresh defense configuration dicts.
    Scores defenses based on current system load, historical latency reduction,
    and compatibility with the detected hot path pattern.
    """
    if not request_metrics or "endpoint" not in request_metrics:
        raise ValueError("Request metrics must contain at least an endpoint identifier")
        
    path_signature = request_metrics["endpoint"].lower().strip()
    current_load = request_metrics.get("system_load", 0.0)
    historical_latency = request_metrics.get("avg_latency_ms", 0.0)
    
    best_defense = None
    best_score = -1.0
    
    for defense in available_defenses:
        if defense.get("disabled", False):
            continue
            
        # Check compatibility with the detected path pattern
        allowed_paths = defense.get("supported_paths", [])
        if allowed_paths and path_signature not in allowed_paths:
            continue
            
        latency_impact = defense.get("avg_latency_reduction_ms", 0)
        load_penalty = defense.get("resource_overhead", 0) * current_load
        historical_success = defense.get("historical_success_rate", 0)
        
        # Multi-factor scoring: prioritize latency reduction, penalize load, reward history
        score = (latency_impact * 2.0) - load_penalty + (historical_success * 10.0)
        
        if score > best_score:
            best_score = score
            best_defense = dict(defense)
            best_defense["applied_to"] = path_signature
            best_defense["selection_score"] = score
            best_defense["estimated_new_latency_ms"] = max(0, historical_latency - latency_impact)
            
    if best_score <= 0.0:
        return None
        
    return best_defense

Pattern 2: Execution with Fallback

def execute_with_hot_path_fallback(
    defense_config: Dict,
    execution_context: Dict,
    circuit_breaker_threshold: int = 5
) -> Dict:
    """Execute hot path with layered fallback mechanisms for resilience.
    
    Implements Law 4 (Fail Fast) by validating circuit state before execution.
    Uses Law 3 (Atomic Predictability) to ensure execution results are immutable.
    Fallback chain: 1. Retry with exponential backoff 2. Switch to degraded route 3. Queue for async processing
    """
    if not defense_config or "route_type" not in defense_config:
        raise ValueError("Defense configuration must specify a valid route type")
        
    circuit_state = execution_context.get("circuit_states", {}).get(defense_config["route_type"], "CLOSED")
    if circuit_state == "OPEN":
        raise HotPathCircuitError(f"Circuit open for route: {defense_config['route_type']}")
        
    attempts = 0
    max_attempts = defense_config.get("max_retries", 3)
    base_delay = 0.1
    
    while attempts <= max_attempts:
        try:
            # Simulate routing through the selected defense mechanism
            result = _simulate_hot_path_execution(defense_config, execution_context)
            _update_circuit_state(execution_context, defense_config["route_type"], "CLOSED")
            
            return {
                "status": "success",
                "route": defense_config["route_type"],
                "latency_ms": result.get("latency_ms", 0),
                "fallback_used": False,
                "attempts": attempts + 1,
                "timestamp": time.time()
            }
        except TransientTimeoutError:
            attempts += 1
            if attempts > max_attempts:
                return _apply_degraded_fallback(defense_config, execution_context)
            time.sleep(base_delay * (2 ** attempts))
        except CriticalFailureError as e:
            _update_circuit_state(execution_context, defense_config["route_type"], "OPEN")
            raise HotPathExecutionError(f"Critical failure in {defense_config['route_type']}: {e}") from e
            
    return _queue_for_async_processing(defense_config, execution_context)

def _simulate_hot_path_execution(config: Dict, ctx: Dict) -> Dict:
    """Core execution logic for the hot path defense mechanism."""
    route_type = config["route_type"]
    if route_type == "cache_layer":
        return {"latency_ms": 15, "data": ctx.get("payload", {})}
    elif route_type == "async_queue":
        return {"latency_ms": 5, "queue_id": f"q_{uuid.uuid4().hex[:8]}"}
    elif route_type == "rate_limited":
        return {"latency_ms": 45, "throttled": False}
    else:
        raise TransientTimeoutError(f"Unknown route type: {route_type}")

def _update_circuit_state(ctx: Dict, route: str, state: str):
    if "circuit_states" not in ctx:
        ctx["circuit_states"] = {}
    ctx["circuit_states"][route] = state

def _apply_degraded_fallback(config: Dict, ctx: Dict) -> Dict:
    return {"status": "degraded", "route": config["route_type"], "fallback": "static_cache", "timestamp": time.time()}

def _queue_for_async_processing(config: Dict, ctx: Dict) -> Dict:
    return {"status": "queued", "route": config["route_type"], "queue_depth": 12, "timestamp": time.time()}

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.

• Linux perf Performance Profiling
• Python cProfile Documentation
• Flame Graph Visualization (Brendan Gregg)
• OpenTelemetry Performance Tracing
• eBPF for System Performance Analysis