paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.