performance-tuning
performance-tuning is an engineering AI skill with a core value of Optimize Linux system performance. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
Quick Facts
mkdir -p ./skills/performance-tuning && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/performance-tuning/SKILL.md -o ./skills/performance-tuning/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Performance Tuning
Optimize Linux system performance through kernel parameter tuning, I/O scheduler selection, memory management, CPU governor configuration, and benchmarking. Covers methodology, real sysctl settings, and tool-based validation.
When to Use
- Server experiencing high latency, throughput bottlenecks, or resource exhaustion
- Preparing infrastructure for high-traffic events or load tests
- Tuning a database server, web server, or application host for production
- Diagnosing whether a bottleneck is CPU, memory, disk I/O, or network
- Establishing baseline performance metrics before and after changes
- Configuring kernel parameters for containers, VMs, or bare-metal hosts
Prerequisites
- Root or sudo access on the target system
- `sysstat` package installed (provides `sar`, `iostat`, `mpstat`)
- `linux-tools` or `perf` package for CPU profiling
- Benchmarking tools: `fio` (disk), `sysbench` (CPU/memory), `iperf3` (network)
- Baseline metrics collected before making any changes
Performance Analysis Methodology
Always follow this order:
1. **Collect baseline** -- measure current performance with tools
2. **Identify bottleneck** -- determine if CPU, memory, I/O, or network
3. **Change one parameter** -- apply a single tuning change
4. **Measure impact** -- re-run the same benchmark
5. **Document** -- record the change and its effect
6. **Iterate or revert** -- keep the change if beneficial, revert if not
System Monitoring Tools
# CPU and process monitoring
top # Interactive process viewer
htop # Enhanced interactive viewer
mpstat -P ALL 2 # Per-CPU utilization every 2 seconds
pidstat -u 2 # Per-process CPU usage
# Memory monitoring
free -h # Memory summary
vmstat 2 # Virtual memory stats every 2 seconds
# Columns: r=runnable, b=blocked, si/so=swap in/out, bi/bo=block I/O
# Disk I/O monitoring
iostat -xz 2 # Extended disk stats every 2 seconds
# Key columns: %util, await (latency), r/s, w/s
iotop -oP # Show processes doing I/O
# Network monitoring
sar -n DEV 2 # Network interface stats
ss -s # Socket summary
nstat # Network counters
# CPU profiling (requires perf)
perf top # Real-time function-level CPU profiling
perf stat -a sleep 10 # System-wide counters for 10 seconds
perf record -g -a sleep 30 # Record 30 seconds of call stacks
perf report # Analyze recorded data
# One-liner: check all major resources
echo "=== CPU ===" && mpstat 1 1 && echo "=== MEM ===" && free -h && echo "=== DISK ===" && iostat -x 1 1 && echo "=== NET ===" && ss -sSysctl Kernel Parameter Tuning
Network Tuning
# /etc/sysctl.d/60-network-performance.conf
# Increase the maximum socket receive/send buffer sizes
net.core.rmem_max = 134217728
net.core.wmem_max = 134217728
net.core.rmem_default = 1048576
net.core.wmem_default = 1048576
# TCP buffer auto-tuning (min, default, max in bytes)
net.ipv4.tcp_rmem = 4096 1048576 134217728
net.ipv4.tcp_wmem = 4096 1048576 134217728
# Increase connection backlog for high-traffic servers
net.core.somaxconn = 65535
net.ipv4.tcp_max_syn_backlog = 65535
net.core.netdev_max_backlog = 65535
# Enable TCP fast open (client and server)
net.ipv4.tcp_fastopen = 3
# Reuse TIME_WAIT sockets for new connections
net.ipv4.tcp_tw_reuse = 1
# Increase the range of ephemeral ports
net.ipv4.ip_local_port_range = 1024 65535
# TCP keepalive tuning (detect dead connections faster)
net.ipv4.tcp_keepalive_time = 120
net.ipv4.tcp_keepalive_intvl = 30
net.ipv4.tcp_keepalive_probes = 3
# Disable slow start after idle (keeps congestion window open)
net.ipv4.tcp_slow_start_after_idle = 0
# Enable BBR congestion control (requires kernel 4.9+)
net.core.defau🎯 Best For
- UX researchers
- Product managers
- Data analysts
- Business intelligence teams
- Claude users
💡 Use Cases
- Mapping user journeys
- Identifying friction points
- Finding patterns in customer data
- Creating automated dashboards
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply performance-tuning to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
Can this analyze user behavior data?
UX research skills work best when you provide session recordings, heatmaps, and analytics data.
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
How do I install performance-tuning?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/performance-tuning/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Over-relying on AI insights
UX decisions should combine AI analysis with direct user feedback and research.
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.