# Error Trace Explainer > Implements intelligent error trace explainer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/error-trace-explainer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/error-trace-explainer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/error-trace-explainer/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/error-trace-explainer --- # Error Trace Explainer Orchestrates intelligent skill selection and execution for error trace explainer 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_trace_patterns( raw_trace: str, known_error_db: List[Dict], severity_threshold: float = 0.6 ) -> Dict: """Analyze raw error trace against known patterns and severity metrics. Implements multi-factor scoring for trace explanation: - Stack frame depth and module relevance - Exception type matching against known failure modes - Historical resolution success rate for similar traces Args: raw_trace: Raw exception traceback string known_error_db: Database of known error patterns with metadata severity_threshold: Minimum confidence to auto-explain Returns: Structured analysis with matched patterns, severity, and explanation draft """ # Guard clause - Early Exit (Law 1) if not raw_trace or not raw_trace.strip(): raise ValueError("Trace cannot be empty") # Parse input - Make Illegal States Unrepresentable (Law 2) parsed_frames = _parse_stack_frames(raw_trace) if not parsed_frames: return {"status": "unparseable", "frames": []} matched_patterns = [] for pattern in known_error_db: score = _calculate_trace_similarity(parsed_frames, pattern) if score >= severity_threshold: matched_patterns.append({ "pattern_id": pattern["id"], "confidence": score, "root_cause": pattern["root_cause"], "suggested_fix": pattern["resolution_steps"] }) # Atomic Predictability (Law 3) - Return new dict return { "trace_id": hashlib.md5(raw_trace.encode()).hexdigest()[:8], "frame_count": len(parsed_frames), "matched_patterns": sorted(matched_patterns, key=lambda x: x["confidence"], reverse=True), "auto_explainable": len(matched_patterns) > 0, "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def generate_trace_explanation( analysis_result: Dict, context: Dict, fallback_strategy: str = "human_review" ) -> Dict: """Generate human-readable trace explanation with resilience fallbacks. Implements Fail Fast, Fail Loud (Law 4): - Unknown traces route to fallback immediately - No partial explanations returned Fallback chain: 1. Use matched pattern explanation 2. Fallback to generic debugging guide 3. Route to human expert with full trace context Args: analysis_result: Output from analyze_trace_patterns context: User environment details (OS, framework, version) fallback_strategy: Routing strategy for unhandled traces Returns: Structured explanation with confidence, steps, and metadata """ # Guard clause - validate analysis (Early Exit) if not analysis_result.get("auto_explainable"): return _route_to_fallback(analysis_result, context, fallback_strategy) # Parse context - Ensure trusted state (Law 2) env_context = _normalize_environment(context) primary_pattern = analysis_result["matched_patterns"][0] try: explanation = _construct_explanation( pattern=primary_pattern, frames=analysis_result.get("frames", []), env=env_context ) # Success - Atomic Predictability (Law 3) return { "success": True, "trace_id": analysis_result["trace_id"], "explanation": explanation["text"], "confidence": primary_pattern["confidence"], "suggested_fixes": primary_pattern["suggested_fix"], "generated_at": time.time() } except TemplateError as e: # Fail Fast - Don't patch malformed explanations (Law 4) return _route_to_fallback(analysis_result, context, fallback_strategy) ``` ### 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 Traceback Documentation]() - [Stack Overflow Error Debugging Guide]() - [ELK Stack for Log Analysis]() - [Sentry Error Tracking Documentation]() - [OpenTelemetry Error Context]()