Agent skill

Hardware Tuning

Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware.

GPL-3.0Auto-check passedDatabases

Install Hardware Tuning

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a claude-code

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

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-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/chmonitor/chmonitor.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/hardware-tuning .claude/skills/hardware-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/

Paste into Claude Code

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .claude/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-tuning --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/chmonitor/chmonitor.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/hardware-tuning .agents/skills/hardware-tuning && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .agents/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-tuning --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/chmonitor/chmonitor.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/hardware-tuning .cursor/skills/hardware-tuning && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .cursor/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/chmonitor/chmonitor.git --path .agents/skills/hardware-tuning

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-tuning --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/chmonitor/chmonitor.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/hardware-tuning .gemini/skills/hardware-tuning && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .gemini/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-tuning

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/chmonitor/chmonitor.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/hardware-tuning .github/skills/hardware-tuning && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .github/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add chmonitor/chmonitor --skill hardware-tuning -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install chmonitor/chmonitor hardware-tuning --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/chmonitor/chmonitor.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/hardware-tuning .opencode/skills/hardware-tuning && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "hardware-tuning" agent skill from https://github.com/chmonitor/chmonitor/tree/main/.agents/skills/hardware-tuning into .opencode/skills/hardware-tuning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hardware-tuning", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: hardware-tuning
GitHub stars: 299
Token cost: ~2.1k tokens
SKILL.md length: 777 words
Files: 1
Skills in repo: 53
Repo updated: Oct 5, 2026
First seen: Oct 7, 2026
Licence: GPL-3.0

At a glance

Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware.

Tasks that involve Data warehousing
SKILL.md covers Detect Hardware, Read Current Settings, Key Settings: Ratio-Based… and Disk Considerations, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Hardware Tuning is an agent skill from chmonitor/chmonitor. Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering Data warehousing. It works with ClickHouse. The repository describes itself as: Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations. The licence is GPL-3.0.

When your agent uses it

Tasks that involve Data warehousing

Example prompts

“/hardware-tuning”

What it can do on your machine

Read from SKILL.md and the folder at commit fc39ef0. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).
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.

Context cost

Hardware Tuning loads about 2.1k tokens when it runs. Until then it costs about 31 tokens; SKILL.md has 777 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~31
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k

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 passed

The automated check found no risky patterns in SKILL.md.

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 chmonitor/chmonitor at commit fc39ef0, republished under its GPL-3.0 licence (© chmonitor). 777 words, ~2,106 tokens.

Download SKILL.mdSave it as .claude/skills/hardware-tuning/SKILL.md (or your agent's skills folder).

name: hardware-tuning
description: Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware.

Hardware Tuning

Load this skill when the user asks "what settings should I change given my server's hardware?" or similar. The workflow is always: detect first, recommend second. Never recommend a specific byte value as a universal truth — give ratios and explain the reasoning.

Detect Hardware

Metric names vary by ClickHouse version. Use broad ILIKE patterns and inspect what is actually returned before drawing conclusions.

sql

-- Step 1: Discover what CPU/memory metrics are available on this server
SELECT metric, value
FROM system.asynchronous_metrics
WHERE metric ILIKE '%CPU%'
   OR metric ILIKE '%Memory%'
   OR metric ILIKE '%core%'
ORDER BY metric

Key metrics to look for (names differ across versions):

What you need	Likely metric names
Logical CPU cores	`CGroupMaxCPU`, `OSCPUVirtualTimeMicroseconds` (indirect), check also `system.metrics` `CPUUsage*`
Total RAM	`OSMemoryTotal`, `CGroupMemoryLimit`
Available RAM	`OSMemoryAvailable`, `OSMemoryFreeWithoutCache`
Used RAM	`OSMemoryUsed`, `MemoryResident`

If neither CGroupMaxCPU nor an obvious cores metric appears, fall back to counting CPU entries from /proc/cpuinfo via SELECT count() FROM system.asynchronous_metrics WHERE metric ILIKE '%CPU%User%' or ask the user directly. Do not fabricate a core count.

sql

-- Step 2: Disk layout — free space and total per disk
SELECT name, path, type,
       formatReadableSize(free_space)  AS free,
       formatReadableSize(total_space) AS total,
       round((1 - free_space / total_space) * 100, 1) AS used_pct
FROM system.disks
ORDER BY total_space DESC

Read Current Settings

Pull the most hardware-sensitive settings so you can compare current vs. recommended:

sql

SELECT name, value, changed, default AS default_value, description
FROM system.settings
WHERE name IN (
    'max_threads',
    'max_insert_threads',
    'max_memory_usage',
    'max_memory_usage_for_user',
    'max_server_memory_usage',
    'max_server_memory_usage_to_ram_ratio',
    'max_bytes_before_external_group_by',
    'max_bytes_before_external_sort',
    'max_bytes_before_external_join',
    'background_pool_size',
    'background_merge_pool_size',
    'background_fetches_pool_size',
    'mark_cache_size',
    'uncompressed_cache_size',
    'max_concurrent_queries',
    'max_connections'
)
ORDER BY name

Note: background_pool_size and background_merge_pool_size are server-level settings (set in config.xml or config.d/). They will appear in system.settings but changing them requires a server restart. Session-level settings take effect immediately with SET.

Key Settings: Ratio-Based Guidance

Use the detected hardware values from the queries above and apply these ratios. Replace <cores> and <RAM_bytes> with the actual numbers you found.

CPU-bound settings

Setting	Scope	Guidance
`max_threads`	session	≈ number of logical cores. Default is auto (`0`). Only lower it if queries compete with each other at high concurrency.
`max_insert_threads`	session	1–4 for normal inserts; up to half of cores for bulk loads.
`background_pool_size`	server (restart)	16 is the default; for merge-heavy workloads raise toward `cores / 2`. Don't exceed cores.
`background_merge_pool_size`	server (restart)	Same guidance as `background_pool_size`. Default 16.
`max_concurrent_queries`	server	Start at `cores * 2`. Lower if queries are large and memory-bound.

Memory-bound settings

Leave genuine headroom — never allocate 100 % of RAM to ClickHouse. The OS, kernel buffers, and other processes need room.

Setting	Scope	Guidance
`max_server_memory_usage`	server	Set to `0` (auto) to use `max_server_memory_usage_to_ram_ratio` instead. If hardcoding: ≤ 80 % of `OSMemoryTotal`.
`max_server_memory_usage_to_ram_ratio`	server	Default `0.9`; consider lowering to `0.8` on shared hosts or when running replicas with ZooKeeper on the same box.
`max_memory_usage`	session	Per-query cap. A common starting point is `RAM × 0.3` for OLAP queries, but tune per workload.
`max_memory_usage_for_user`	session	Per-user sum cap. Useful when multiple users share the server; set to `RAM × 0.5` as a starting point.
`max_bytes_before_external_group_by`	session	Spill threshold for GROUP BY. Typically half of `max_memory_usage`. If set to `0`, spilling is disabled.
`max_bytes_before_external_sort`	session	Same pattern as external group by. Setting this too low causes unnecessary disk spilling; too high causes OOM.
`max_bytes_before_external_join`	session	Applies to hash joins. Same ratio guidance.

Show full SKILL.md (340 more words)Show less

Cache settings (server-level, config.xml or `<cache>` section)

These affect how much RAM is reserved for ClickHouse's internal caches. Changing them requires restart.

Setting	Guidance
`mark_cache_size`	Default 5 GiB. For servers with lots of RAM (≥ 64 GiB) and many columns, raise to 10–20 GiB. Do not exceed ≈ 10 % of RAM.
`uncompressed_cache_size`	Default 0 (disabled). Only enable if you have a read-heavy workload with repeated small queries on the same columns. Cap at ≈ 5–10 % of RAM.

Disk Considerations

After running the disk query above:

Single spinning disk: reduce background_pool_size and background_merge_pool_size to avoid I/O saturation during merges. Values of 4–8 are common.
NVMe / SSD array: defaults or higher pool sizes are fine.
Tiered storage (hot/cold): ensure the hot tier has enough free space for active merges (ClickHouse needs ≈ 2× the size of the parts being merged).
Disk nearly full (> 90 % used): inserts will stall. Investigate via system.parts and consider OPTIMIZE … FINAL or archiving old partitions before tuning anything else.

Caution

Change one setting at a time and measure before moving to the next.
Defaults are often correct. ClickHouse auto-detects cores and sets max_threads = 0 (auto). Only override when you have a concrete reason.
Server-level vs. session-level: background_pool_size, mark_cache_size, max_server_memory_usage, and max_concurrent_queries require editing config.xml (or a file in config.d/) and restarting the server. Session-level settings (max_threads, max_memory_usage, etc.) apply immediately with SET or in the query profile.
Replicated environments: changing server-level settings must be applied consistently to all replicas. Inconsistent pool sizes can cause one replica to fall behind on merges.
ClickHouse Keeper / ZooKeeper on the same host: the coordination process can consume significant RAM under load. Budget at least 4–8 GiB for it and reduce max_server_memory_usage_to_ram_ratio accordingly.

Verification

After applying changes, confirm they took effect and watch for regressions:

sql

-- Confirm the setting is now the value you set (session-level)
SELECT name, value, changed
FROM system.settings
WHERE name IN ('max_threads', 'max_memory_usage', 'max_bytes_before_external_group_by')

-- Watch for OOM or memory-allocation errors after a change
SELECT name, value AS error_count, last_error_time, last_error_message
FROM system.errors
WHERE name ILIKE '%Memory%' OR name ILIKE '%Alloc%'
ORDER BY last_error_time DESC
LIMIT 20

-- Monitor peak memory usage on currently running queries
SELECT query_id, user, memory_usage, peak_memory_usage,
       formatReadableSize(memory_usage)      AS mem,
       formatReadableSize(peak_memory_usage) AS peak_mem,
       elapsed,
       substring(query, 1, 120)              AS query
FROM system.processes
WHERE query NOT LIKE '%processes%'
ORDER BY peak_memory_usage DESC

If system.errors shows MEMORY_LIMIT_EXCEEDED spikes after a change, roll back that setting before adjusting others.

Cross-references

clickhouse-best-practices — production insert, cache, and connection-pool guidance
schema-design-advisor — part counts and merge behavior affect background pool sizing
troubleshooting — error-code-driven diagnosis including OOM and disk-full incidents
query-tuning-advisor — per-query memory and parallelism knobs once server settings are stable

Files

Just SKILL.md in .agents/skills/hardware-tuning of chmonitor/chmonitor.

Open the folder on GitHubat commit fc39ef0

Compare with similar skills

Hardware 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.

Hardware Tuning compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Hardware Tuning this skillchmonitor/chmonitor	299	—	~2.1k	Automated safety check: Pass	GPL-3.0	2 days ago
Keeper Stress AnalysisClickHouse/ClickHouse	50k	—	~4.7k	Automated safety check: Pass	Apache-2.0	today
Perf ComparisonClickHouse/ClickHouse	50k	—	~3.9k	Automated safety check: Notes	Apache-2.0	today
Patch Release CheckClickHouse/ClickHouse	50k	—	~4k	Automated safety check: Notes	Apache-2.0	today
Clickhouse Architecture Advisorvemetric/vemetric	394	2 repos	~791	Automated safety check: Pass	Apache-2.0	yesterday
Decompress BinaryClickHouse/ClickHouse	50k	—	~1.1k	Automated safety check: Pass	Apache-2.0	today

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Works with

ClickHouse

Questions about Hardware Tuning

What does Hardware Tuning do?

Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware. Hardware Tuning is an agent skill from chmonitor/chmonitor. Detect server cores/RAM/disk from system tables, then recommend ClickHouse settings sized to that hardware.

When should I use Hardware Tuning?

Hardware Tuning fits situations like: tasks that involve Data warehousing.

How do I install Hardware Tuning in Claude Code?

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

How do I install Hardware Tuning in Codex?

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

Can I use Hardware 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 chmonitor/chmonitor --skill hardware-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/hardware-tuning, .gemini/skills/hardware-tuning, .github/skills/hardware-tuning and .opencode/skills/hardware-tuning in your project.

What does Hardware Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Hardware Tuning is instructions for the agent only.

Does Hardware 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 Hardware Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hardware Tuning use?

Hardware Tuning is published under the GPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hardware Tuning use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 Hardware Tuning?

Skills that share tags, products or a category with Hardware Tuning: Keeper Stress Analysis (ClickHouse/ClickHouse, 50k stars), Perf Comparison (ClickHouse/ClickHouse, 50k stars), Patch Release Check (ClickHouse/ClickHouse, 50k stars) and Clickhouse Architecture Advisor (vemetric/vemetric, 394 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hardware Tuning?

chmonitor (a GitHub organization) maintains it in chmonitor/chmonitor, which has 299 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on October 5, 2026.

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

Install Hardware Tuning

Facts

At a glance

What it does

When your agent uses it

Example prompts

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Hardware Tuning

Detect Hardware

Read Current Settings

Key Settings: Ratio-Based Guidance

CPU-bound settings

Memory-bound settings

Cache settings (server-level, config.xml or <cache> section)

Disk Considerations

Caution

Verification

Cross-references

Files

Compare with similar skills

Similar skills

More from chmonitor/chmonitor

Related

Questions about Hardware Tuning

Cache settings (server-level, config.xml or `<cache>` section)