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

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

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