# Hot Path Detector > Implements intelligent hot path detector with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/hot-path-detector` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/hot-path-detector` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/hot-path-detector/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/hot-path-detector --- # 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 ```python 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 ```python 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 | | --- --- ## 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]()