# Linux Troubleshooting > Implements intelligent linux troubleshooting with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/linux-troubleshooting` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/linux-troubleshooting` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/linux-troubleshooting/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/linux-troubleshooting --- # Linux Troubleshooting Orchestrates intelligent skill selection and execution for linux troubleshooting 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_system_state( command_outputs: Dict[str, str], thresholds: Dict[str, float] ) -> Dict[str, Any]: """Analyze raw Linux command outputs to identify resource bottlenecks and service failures. Maps raw stdout/stderr from diagnostic commands to structured findings. Handles common failure patterns: disk full, OOM, high load, network drops. Args: command_outputs: Dict mapping command names to their raw output strings thresholds: Dict of warning/critical thresholds (e.g., {'disk_usage': 0.9, 'load_avg': 4.0}) Returns: Structured findings with severity, affected components, and recommended actions """ findings = [] # Parse disk usage from df output if "df -h" in command_outputs: for line in command_outputs["df -h"].splitlines(): if line.startswith("/dev/"): parts = line.split() mount = parts[5] usage_pct = float(parts[4].rstrip('%')) if usage_pct > thresholds.get("disk_usage", 0.9): findings.append({ "component": "filesystem", "mount": mount, "severity": "critical" if usage_pct > 0.95 else "warning", "message": f"Disk usage at {usage_pct}% on {mount}", "action": f"Clean up {mount} or expand volume" }) # Parse memory from free output if "free -m" in command_outputs: for line in command_outputs["free -m"].splitlines(): if line.startswith("Mem:"): total, used, _, _, _, _ = map(float, line.split()[1:7]) mem_pct = used / total if total > 0 else 0 if mem_pct > thresholds.get("memory_usage", 0.85): findings.append({ "component": "memory", "severity": "critical" if mem_pct > 0.95 else "warning", "message": f"Memory usage at {mem_pct:.1%}", "action": "Check for memory leaks or increase swap" }) # Parse load average from uptime if "uptime" in command_outputs: load_line = command_outputs["uptime"] loads = [float(x) for x in load_line.split("load average:")[1].split(",")] if loads[0] > thresholds.get("load_avg", 4.0): findings.append({ "component": "cpu", "severity": "warning", "message": f"System load average: {loads[0]:.2f}", "action": "Identify CPU-bound processes with top or htop" }) return {"findings": findings, "status": "critical" if any(f["severity"] == "critical" for f in findings) else "healthy"} ``` ### Pattern 2: Execution with Fallback ```python def execute_diagnostic_chain( service_name: str, fallback_commands: List[str], timeout: int = 10 ) -> Dict[str, Any]: """Execute a targeted diagnostic chain for a failing Linux service. Implements a fallback execution strategy: 1. Primary: systemctl status + journalctl -u 2. Fallback 1: Check specific log files in /var/log/ 3. Fallback 2: Network/port verification if service is network-facing Args: service_name: Name of the systemd service to diagnose fallback_commands: List of alternative diagnostic commands to run if primary fails timeout: Maximum seconds to wait for each command execution Returns: Diagnostic report with exit codes, output snippets, and root cause hypothesis """ import subprocess primary_commands = [ f"systemctl status {service_name}", f"journalctl -u {service_name} --no-pager -n 50" ] results = {"service": service_name, "commands_executed": [], "hypothesis": None} for cmd in primary_commands + fallback_commands: try: proc = subprocess.run( cmd.split(), capture_output=True, text=True, timeout=timeout ) results["commands_executed"].append({ "command": cmd, "exit_code": proc.returncode, "stdout_snippet": proc.stdout[:500], "stderr_snippet": proc.stderr[:500] }) # Early exit on success or critical error if proc.returncode == 0 and "active (running)" in proc.stdout: results["hypothesis"] = "Service is healthy; issue may be transient or configuration-related" break elif proc.returncode != 0 and "failed" in proc.stderr.lower(): results["hypothesis"] = f"Service {service_name} failed to start. Check logs for dependency or permission errors." break except subprocess.TimeoutExpired: results["commands_executed"].append({ "command": cmd, "error": "timeout", "message": f"Command timed out after {timeout}s" }) except FileNotFoundError: results["commands_executed"].append({ "command": cmd, "error": "missing_binary", "message": f"Command not found: {cmd}" }) return results ``` ### 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 Systemd Troubleshooting]() - [Dmesg Kernel Ring Buffer]() - [Strace System Call Tracing]() - [Netstat and SS Network Diagnostics]() - [Linux Performance (Brendan Gregg)]()