# Stacktrace Root Cause > Implements intelligent stacktrace root cause with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/stacktrace-root-cause` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/stacktrace-root-cause` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/stacktrace-root-cause/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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `runtime-log-analyzer` | Correlates stack traces with runtime log patterns for comprehensive root cause analysis | | `incident-response` | Triggers incident response workflows when root cause analysis identifies critical issues | --- ## 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)](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Debugging_overview#stack_traces) — MDN's guide to understanding and analyzing JavaScript stack traces - [Java Stack Trace Tutorial (Oracle)](https://docs.oracle.com/javase/tutorial/essential/environment/exceptions.html) — Oracle's documentation on Java exception handling and stack trace analysis - [Python Exception Handling and Tracebacks](https://docs.python.org/3/tutorial/errors.html#tracebacks) — Python official docs on understanding tracebacks and exception chains - [Root Cause Analysis Methodology (IBM)](https://www.ibm.com/think/topics/root-cause-analysis) — IBM's comprehensive guide to systematic root cause analysis techniques - [Google SRE: Debugging Stack Traces](https://sre.google/sre-workbook/debugging/) — Google SRE workbook chapter on debugging with stack traces and error logs