Agent skill

Performance Tuning

by sickn33 in sickn33/agentic-awesome-skills

Optimize Linux system performance. An agent skill from sickn33/agentic-awesome-skills.

MITAuto-check: notes

Install Performance Tuning

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill performance-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills performance-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performance-tuning .claude/skills/performance-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performance-tuning
GitHub stars
47k
Used in
2 other repos
Token cost
~3.2k tokens
SKILL.md length
400 words
Files
1
Skills in repo
1,354
Repo updated
First seen
Licence
MIT

At a glance

Optimize Linux system performance. An agent skill from sickn33/agentic-awesome-skills.

  • Works in 6 steps: Collect baseline -- measure current… → Identify bottleneck -- determine if CPU,… → Change one parameter -- apply a single… → …
  • Improving system performance
  • SKILL.md covers When to Use, Prerequisites, Performance Analysis Methodology and System Monitoring Tools, plus 8 more sections
  • Calls apt and dnf

What it does

Performance Tuning is an agent skill from sickn33/agentic-awesome-skills. Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

It works with Linux. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Improving system performance

Example prompts

  • “/performance-tuning”

Requirements

  • Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit ec02547. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • apt
    • dnf

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.

    From compatibility in the SKILL.md frontmatter.

Context cost

Performance Tuning loads about 3.2k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 400 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteRuns commands with sudoSKILL.md:35
    - Root or sudo access on the target system

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit ec02547, republished under its MIT licence (© sickn33). 400 words, ~3,201 tokens.

Download SKILL.mdSave it as .claude/skills/performance-tuning/SKILL.md (or your agent's skills folder).
name
performance-tuning
description
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
compatibility
Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
category
devops
risk
critical
source
https://github.com/BagelHole/DevOps-Security-Agent-Skills
source_repo
BagelHole/DevOps-Security-Agent-Skills
source_type
community
date_added
2026-09-20
license
MIT
license_source
https://github.com/BagelHole/DevOps-Security-Agent-Skills/blob/main/LICENSE
metadata.author
devops-skills
metadata.version
1.0

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

bash
# 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 -s

Sysctl Kernel Parameter Tuning

Network Tuning
bash
# /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.default_qdisc = fq
net.ipv4.tcp_congestion_control = bbr
Memory Tuning
bash
# /etc/sysctl.d/60-memory-performance.conf

# Reduce swappiness (0-100, lower = less swap usage)
# 10 for general servers, 1 for database servers
vm.swappiness = 10

# Dirty page ratios (controls when dirty data is flushed to disk)
# Lower values = more frequent, smaller writes (better for SSDs)
vm.dirty_ratio = 20
vm.dirty_background_ratio = 5

# For large-memory systems writing to fast storage
# vm.dirty_ratio = 40
# vm.dirty_background_ratio = 10

# Increase inotify limits (for apps watching many files)
fs.inotify.max_user_watches = 524288
fs.inotify.max_user_instances = 1024

# Maximum number of open file descriptors system-wide
fs.file-max = 2097152

# Virtual memory overcommit
# 0 = heuristic (default), 1 = always overcommit, 2 = never overcommit
vm.overcommit_memory = 0

# For Redis or similar in-memory stores, use:
# vm.overcommit_memory = 1

# Disable Transparent Huge Pages if it causes latency spikes (common with databases)
# Done via boot parameter or runtime:
# echo madvise > /sys/kernel/mm/transparent_hugepage/enabled
Applying Sysctl Changes
bash
# Apply all sysctl files
sysctl --system

# Apply a specific file
sysctl -p /etc/sysctl.d/60-network-performance.conf

# Set a parameter temporarily (lost on reboot)
sysctl -w vm.swappiness=10

# Verify a parameter
sysctl vm.swappiness
sysctl net.ipv4.tcp_congestion_control

I/O Scheduler Configuration

bash
# Check the current scheduler for a device
cat /sys/block/sda/queue/scheduler
# Output example: [mq-deadline] none kyber bfq

# Set the scheduler temporarily
echo mq-deadline > /sys/block/sda/queue/scheduler   # Good for databases
echo none > /sys/block/nvme0n1/queue/scheduler       # Best for NVMe SSDs
echo bfq > /sys/block/sda/queue/scheduler            # Good for interactive desktop

# Scheduler recommendations:
# NVMe SSD:  none (noop)    -- minimal overhead, hardware handles scheduling
# SATA SSD:  mq-deadline    -- provides fairness with low latency
# HDD:       mq-deadline    -- prevents starvation, good for databases
# Desktop:   bfq            -- prioritizes interactive I/O

# Make persistent via udev rule
cat <<'EOF' > /etc/udev/rules.d/60-io-scheduler.rules
# Set mq-deadline for rotational (HDD) devices
ACTION=="add|change", KERNEL=="sd[a-z]", ATTR{queue/rotational}=="1", ATTR{queue/scheduler}="mq-deadline"
# Set none for non-rotational (SSD/NVMe) devices
ACTION=="add|change", KERNEL=="sd[a-z]", ATTR{queue/rotational}=="0", ATTR{queue/scheduler}="none"
ACTION=="add|change", KERNEL=="nvme[0-9]*", ATTR{queue/scheduler}="none"
EOF

udevadm control --reload-rules

# Tune read-ahead for sequential workloads (database sequential scans)
blockdev --setrahead 4096 /dev/sda    # 4096 sectors = 2 MB

# Enable TRIM for SSDs (weekly via systemd timer)
systemctl enable --now fstrim.timer
fstrim -av    # Manual run

CPU Governor Configuration

bash
# Check available governors
cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_available_governors
# Output: performance powersave schedutil

# Check current governor
cat /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor

# Set all CPUs to performance mode (maximum frequency)
for cpu in /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor; do
  echo performance > "$cpu"
done

# Set using cpupower (if installed)
cpupower frequency-set -g performance

# Governor recommendations:
# Server (production):   performance    -- max frequency, lowest latency
# Server (general):      schedutil      -- kernel-driven dynamic scaling
# Laptop / idle server:  powersave      -- minimize power consumption

# Make persistent via systemd service
cat <<'EOF' > /etc/systemd/system/cpu-governor.service
[Unit]
Description=Set CPU governor to performance

[Service]
Type=oneshot
ExecStart=/usr/bin/cpupower frequency-set -g performance
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target
EOF

systemctl enable --now cpu-governor

# Disable CPU boost (turbo) if consistent latency is needed
echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo

Benchmarking Tools

fio -- Disk I/O Benchmarking
bash
# Install fio
apt install -y fio    # Debian/Ubuntu
dnf install -y fio    # RHEL/CentOS

# Sequential read test (simulates backup reads)
fio --name=seq-read --ioengine=libaio --direct=1 --rw=read \
  --bs=1M --numjobs=4 --size=1G --runtime=60 --time_based --group_reporting

# Sequential write test
fio --name=seq-write --ioengine=libaio --direct=1 --rw=write \
  --bs=1M --numjobs=4 --size=1G --runtime=60 --time_based --group_reporting

# Random read (4K blocks -- simulates database IOPS)
fio --name=rand-read --ioengine=libaio --direct=1 --rw=randread \
  --bs=4k --numjobs=16 --iodepth=64 --size=1G --runtime=60 --time_based --group_reporting

# Random write (4K blocks)
fio --name=rand-write --ioengine=libaio --direct=1 --rw=randwrite \
  --bs=4k --numjobs=16 --iodepth=64 --size=1G --runtime=60 --time_based --group_reporting

# Mixed random read/write (70/30 -- typical database workload)
fio --name=mixed --ioengine=libaio --direct=1 --rw=randrw --rwmixread=70 \
  --bs=4k --numjobs=8 --iodepth=32 --size=1G --runtime=60 --time_based --group_reporting
sysbench -- CPU and Memory Benchmarking
bash
# Install: apt install -y sysbench (Debian) / dnf install -y sysbench (RHEL)

# CPU benchmark
sysbench cpu --threads=4 --time=30 run

# Memory benchmark
sysbench memory --threads=4 --time=30 --memory-block-size=1K --memory-total-size=100G run
iperf3 -- Network Benchmarking
bash
# Install iperf3
apt install -y iperf3

# Start server on one host
iperf3 -s

# Run client test from another host
iperf3 -c <server-ip> -t 30 -P 4    # 30 seconds, 4 parallel streams

# Test with UDP (measure packet loss)
iperf3 -c <server-ip> -u -b 1G -t 30

# Reverse mode (server sends to client)
iperf3 -c <server-ip> -R -t 30

Quick-Reference Tuning Profiles

Web Server (nginx/Apache)
bash
# /etc/sysctl.d/60-webserver.conf
net.core.somaxconn = 65535
net.ipv4.tcp_max_syn_backlog = 65535
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_fastopen = 3
net.ipv4.ip_local_port_range = 1024 65535
net.core.default_qdisc = fq
net.ipv4.tcp_congestion_control = bbr
fs.file-max = 2097152
vm.swappiness = 10
Database Server (PostgreSQL/MySQL)
bash
# /etc/sysctl.d/60-database.conf
vm.swappiness = 1
vm.dirty_ratio = 15
vm.dirty_background_ratio = 3
vm.overcommit_memory = 2
vm.overcommit_ratio = 80
net.core.somaxconn = 4096
fs.file-max = 2097152
# Disable THP for databases
# echo never > /sys/kernel/mm/transparent_hugepage/enabled
Show full SKILL.md (171 more words)Show less

Troubleshooting

SymptomDiagnostic CommandCommon Fix
High CPU, no obvious processperf top, mpstat -P ALL 2Check for kernel-level issues: softirqs, interrupts
High load avg, low CPU usagevmstat 2 (check b column)I/O bottleneck: tune scheduler, check disk health
System swapping heavilyfree -h, vmstat 2 (check si/so)Reduce vm.swappiness, add RAM, find memory leak
Disk latency spikesiostat -x 2 (check await)Switch I/O scheduler, reduce dirty ratio, add SSD
"Too many open files" errorcat /proc/sys/fs/file-nrIncrease fs.file-max and LimitNOFILE
Network throughput lowiperf3 -c <server>, ethtoolIncrease buffer sizes, enable BBR, check MTU
Application timeout under loadss -s, sysctl net.core.somaxconnIncrease somaxconn and tcp_max_syn_backlog
Inconsistent latencyCheck CPU governorSet governor to performance, disable turbo boost
  • linux-administration -- General system monitoring and management
  • systemd-services -- Resource limits via cgroups in unit files
  • block-storage -- Storage-level performance (LVM, RAID, filesystems)
  • nfs-storage -- NFS-specific performance tuning

Limitations

  • Infrastructure commands can disrupt services: confirm target host/scope and have backups/snapshots before mutating state.
  • Docs-only import: upstream scripts and templates not bundled.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/performance-tuning of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit ec02547

Used in 2 other repositories

We found 6 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Performance Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Performance Tuning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Performance Tuning this skillsickn33/agentic-awesome-skills47k2 repos~3.2kAutomated safety check: NotesMIT
Configuring Horizoncoollabsio/coolify63k4 repos~898Automated safety check: PassMIT
Engine Whats Newflutter/flutter179k—~978Automated safety check: PassBSD-3-Clause
Openclaw Live Updateropenclaw/openclaw392k—~3.7kAutomated safety check: PassMIT
Upgrade Browserflutter/flutter179k—~1.1kAutomated safety check: PassBSD-3-Clause
K8s Security PoliciesCybereason-Public/owLSM28012 repos~2kAutomated safety check: PassGPL-2.0

Similar skills

  • Configuring Horizon

    coollabsio/coolify

    A skill your agent uses whenever the user mentions Horizon by name in a Laravel context.

    63k GitHub starsUsed in 4 repos~898 tokens
    Backend & APIsAuto-check passed
  • Engine Whats New

    flutter/flutter

    Generates the "what's new" release summary and diff file for changes in the Flutter engine (//engine/src/flutter) between two releases (e.g., 3.47 vs 3.44).

    179k GitHub stars~978 tokensUpdated today
    MobileAuto-check passed
  • Openclaw Live Updater

    openclaw/openclaw

    Maintain the canonical live OpenClaw main checkout, macOS LaunchAgent-managed Gateway, local macOS app, exact-head main CI, and recurring full release validation.

    392k GitHub stars~3.7k tokensUpdated today
    DevOps & CloudAuto-check passed
  • Upgrade Browser

    flutter/flutter

    Upgrade browser versions (Chrome or Firefox) in the Flutter Web Engine and/or Framework tests.

    179k GitHub stars~1.1k tokensUpdated today
    MobileAuto-check passed
  • K8s Security Policies

    Cybereason-Public/owLSM

    Comprehensive guide for implementing NetworkPolicy, PodSecurityPolicy, RBAC, and Pod Security Standards in Kubernetes.

    280 GitHub starsUsed in 12 repos~2k tokens
    Backend & APIsAuto-check passed
  • Apple Container Test Runner

    RustPython/RustPython

    Runs RustPython tests inside a Linux container built with Apple's container CLI, so macOS users can compare Linux results with their local ones.

    22k GitHub stars~467 tokensUpdated today
    Testing & QAAuto-check passed

More from sickn33/agentic-awesome-skills

All 1,354 skills in this repo
  • Liuguang Banlan UI

    sickn33/agentic-awesome-skills

    Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • User Thoughts Memory

    sickn33/agentic-awesome-skills

    Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Using LWC Memory and Graphs

    sickn33/agentic-awesome-skills

    Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.

    47k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed
  • Find Complementary Founders

    sickn33/agentic-awesome-skills

    Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.

    47k GitHub starsUsed in 1 repo~4.8k tokens
    Auto-check passed
  • Cline Pilot

    sickn33/agentic-awesome-skills

    Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.

    47k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed
  • Content Creator

    sickn33/agentic-awesome-skills

    Drafts and reviews audience-specific content from supplied brand examples, with local scripts for brand voice and SEO diagnostics, channel templates and a content calendar.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed

Works with

Questions about Performance Tuning

What does Performance Tuning do?

Optimize Linux system performance. An agent skill from sickn33/agentic-awesome-skills. Performance Tuning is an agent skill from sickn33/agentic-awesome-skills. Optimize Linux system performance.

When should I use Performance Tuning?

Performance Tuning fits situations like: improving system performance.

How do I install Performance Tuning in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill performance-tuning -a claude-code`. Or copy the skill folder (skills/performance-tuning in sickn33/agentic-awesome-skills) into .claude/skills/performance-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Performance Tuning in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill performance-tuning -a codex`. Or copy the skill folder (skills/performance-tuning in sickn33/agentic-awesome-skills) into .agents/skills/performance-tuning in your project. Codex loads it when a task matches its description.

Can I use Performance Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill performance-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-tuning, .gemini/skills/performance-tuning, .github/skills/performance-tuning and .opencode/skills/performance-tuning in your project.

What does Performance Tuning need to run?

Going by SKILL.md and its folder, Performance Tuning needs the command-line tools its instructions call (apt and dnf). Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled..

Does Performance Tuning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Performance Tuning safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Performance Tuning use?

Performance Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performance Tuning use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Performance Tuning?

Skills that share tags, products or a category with Performance Tuning: Configuring Horizon (coollabsio/coolify, 63k stars), Engine Whats New (flutter/flutter, 179k stars), Openclaw Live Updater (openclaw/openclaw, 392k stars) and Upgrade Browser (flutter/flutter, 179k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performance Tuning?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,343 GitHub stars. The repository holds 1,354 skills in this directory. The repository was last updated on October 7, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.