Linux Troubleshooting

Linux Troubleshooting Workflow

Overview

Specialized workflow for diagnosing and resolving Linux system issues including performance problems, service failures, network issues, and resource constraints.

When to Use This Workflow

Use this workflow when:

• Diagnosing system performance issues
• Troubleshooting service failures
• Investigating network problems
• Resolving disk space issues
• Debugging application errors

Workflow Phases

Phase 1: Initial Assessment

Skills to Invoke

• bash-linux - Linux commands
• devops-troubleshooter - Troubleshooting

Actions

1. Check system uptime
2. Review recent changes
3. Identify symptoms
4. Gather error messages
5. Document findings

Commands

uptime
hostnamectl
cat /etc/os-release
dmesg | tail -50

Copy-Paste Prompts

Use @bash-linux to gather system information

Phase 2: Resource Analysis

Skills to Invoke

• bash-linux - Resource commands
• performance-engineer - Performance analysis

Actions

1. Check CPU usage
2. Analyze memory
3. Review disk space
4. Monitor I/O
5. Check network

Commands

top -bn1 | head -20
free -h
df -h
iostat -x 1 5

Copy-Paste Prompts

Use @performance-engineer to analyze system resources

Phase 3: Process Investigation

Skills to Invoke

• bash-linux - Process commands
• server-management - Process management

Actions

1. List running processes
2. Identify resource hogs
3. Check process status
4. Review process trees
5. Analyze strace output

Commands

ps aux --sort=-%cpu | head -10
pstree -p
lsof -p PID
strace -p PID

Copy-Paste Prompts

Use @server-management to investigate processes

Phase 4: Log Analysis

Skills to Invoke

• bash-linux - Log commands
• error-detective - Error detection

Actions

1. Check system logs
2. Review application logs
3. Search for errors
4. Analyze log patterns
5. Correlate events

Commands

journalctl -xe
tail -f /var/log/syslog
grep -i error /var/log/*

Copy-Paste Prompts

Use @error-detective to analyze log files

Phase 5: Network Diagnostics

Skills to Invoke

• bash-linux - Network commands
• network-engineer - Network troubleshooting

Actions

1. Check network interfaces
2. Test connectivity
3. Analyze connections
4. Review firewall rules
5. Check DNS resolution

Commands

ip addr show
ss -tulpn
curl -v http://target
dig domain

Copy-Paste Prompts

Use @network-engineer to diagnose network issues

Phase 6: Service Troubleshooting

Skills to Invoke

• server-management - Service management
• systematic-debugging - Debugging

Actions

1. Check service status
2. Review service logs
3. Test service restart
4. Verify dependencies
5. Check configuration

Commands

systemctl status service
journalctl -u service -f
systemctl restart service

Copy-Paste Prompts

Use @systematic-debugging to troubleshoot service issues

Phase 7: Resolution

Skills to Invoke

• incident-responder - Incident response
• bash-pro - Fix implementation

Actions

1. Implement fix
2. Verify resolution
3. Monitor stability
4. Document solution
5. Create prevention plan

Copy-Paste Prompts

Use @incident-responder to implement resolution

Troubleshooting Checklist

• System information gathered
• Resources analyzed
• Logs reviewed
• Network tested
• Services verified
• Issue resolved
• Documentation created

Quality Gates

• Root cause identified
• Fix verified
• Monitoring in place
• Documentation complete

Related Workflow Bundles

• os-scripting - OS scripting
• bash-scripting - Bash scripting
• cloud-devops - DevOps

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Linux Troubleshooting"

Storing Results

After completing work, store workflow/automation decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags linux-troubleshooting workflow

Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>

Playbook Engine

Combine this skill with others using the Playbook Engine (execution/workflow_engine.py) for guided multi-step automation with progress tracking.