Stacktrace Root Cause

Stacktrace Root Cause

Orchestrates intelligent skill selection and execution for stacktrace root cause 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

import re
from dataclasses import dataclass
from typing import List, Optional, Dict

@dataclass
class StackFrame:
    module: str
    function: str
    line: int
    file: str
    is_root_cause: bool = False

def parse_stacktrace(raw_trace: str) -> List[StackFrame]:
    """Parse raw stacktrace string into structured frames.
    Identifies root cause by finding the deepest application-level frame
    that matches known error signatures or exception types.
    """
    if not raw_trace or not raw_trace.strip():
        raise ValueError("Stacktrace cannot be empty")

    frames = []
    frame_pattern = re.compile(r"^\s*at\s+([\w.$]+)\.([\w$]+)\(([^:]+):(\d+)\)")

    for line in raw_trace.splitlines():
        match = frame_pattern.match(line)
        if match:
            module, func, file, line_num = match.groups()
            frames.append(StackFrame(
                module=module,
                function=func,
                line=int(line_num),
                file=file
            ))

    if not frames:
        return []

    # Identify root cause: typically the deepest frame before framework wrappers
    for i in range(len(frames) - 1, -1, -1):
        frame = frames[i]
        if any(frame.module.startswith(prefix) for prefix in ("java.", "javax.", "sun.", "org.springframework.", "com.google.")):
            continue
        frames[i].is_root_cause = True
        break

    return frames

Pattern 2: Execution with Fallback

def analyze_root_cause(frames: List[StackFrame], error_context: Dict) -> Dict:
    """Analyze parsed stacktrace frames to determine root cause and generate fix recommendations.
    Matches frames against known error signatures and applies domain-specific heuristics.
    """
    if not frames:
        return {"status": "unparsable", "message": "No valid frames found"}

    root_frame = next((f for f in frames if f.is_root_cause), frames[-1])
    error_type = error_context.get("exception_type", "UnknownError")
    signature_key = f"{root_frame.module}.{root_frame.function}"

    known_issues = _lookup_error_signature(signature_key, error_type)
    if known_issues:
        return {
            "status": "matched",
            "root_cause": known_issues["description"],
            "suggested_fix": known_issues["fix"],
            "confidence": known_issues["confidence"],
            "affected_module": root_frame.module,
            "file": root_frame.file,
            "line": root_frame.line
        }

    # Fallback heuristic analysis for unknown errors
    return {
        "status": "heuristic_analysis",
        "root_cause": f"Unrecognized error in {root_frame.module}.{root_frame.function}",
        "suggested_fix": "Review recent changes to the affected module. Check for null references, boundary condition failures, or dependency version mismatches.",
        "confidence": 0.65,
        "affected_module": root_frame.module,
        "file": root_frame.file,
        "line": root_frame.line
    }

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

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 domain. The model follows markdown links at load time to resolve external references and inline content.

• Stack Trace Analysis Guide (Mozilla Developer Network) — MDN's guide to understanding and analyzing JavaScript stack traces
• Java Stack Trace Tutorial (Oracle) — Oracle's documentation on Java exception handling and stack trace analysis
• Python Exception Handling and Tracebacks — Python official docs on understanding tracebacks and exception chains
• Root Cause Analysis Methodology (IBM) — IBM's comprehensive guide to systematic root cause analysis techniques
• Google SRE: Debugging Stack Traces — Google SRE workbook chapter on debugging with stack traces and error logs