---

name: linux-troubleshooting compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent linux troubleshooting with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: linux-troubleshooting, linux troubleshooting, how do i linux-troubleshooting, orchestrate linux-troubleshooting, automate linux-troubleshooting, agent linux-troubleshooting archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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 | |

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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)