generate-tuned-profile

generate-tuned-profile is a command for Claude Code from wangke19/gemini-ai-helpers. It costs 21 tokens per session (2,337 once invoked), scanned A, a copy of generate-tuned-profile, Apache-2.0.

A command that creates a YAML configuration file for the OpenShift Node Tuning Operator, which applies CPU, memory, kernel, and service settings to selected nodes.

In plain words
What is it for?
It helps create Tuned profiles with sysctl, bootloader, service, and node-selection settings for version-controlled cluster configuration.
Why use it?
It avoids manually assembling multi-line YAML and reduces mistakes when defining tuning settings and node-matching rules.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: positional $N argument.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 extensions/node-tuning/skills/scripts/generate_tuned_profile.py \.

Good fit It helps create Tuned profiles with sysctl, bootloader, service, and node-selection settings for version-controlled cluster configuration.

Compare 6 commands from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/wangke19/gemini-ai-helpers
agentmods
npx agentmods add commands/wangke19/gemini-ai-helpers/generate-tuned-profile

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for generate-tuned-profile

README.md
[![agentmods](https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/generate-tuned-profile/github.svg)](https://agentmods.dev/commands/wangke19/gemini-ai-helpers/generate-tuned-profile)
Your own site
<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/generate-tuned-profile"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/generate-tuned-profile/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for generate-tuned-profile

Your own site · 80×15
<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/generate-tuned-profile"><img src="https://agentmods.dev/badge/commands/wangke19/gemini-ai-helpers/generate-tuned-profile.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00021 $0.02337
Opus 5 $0.00010 $0.01169
Sonnet 5 $0.00004 $0.00467
Haiku 4.5 $0.00002 $0.00234

Measured 8d ago against content hash 1b8a610dbec9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

generate-tuned-profile scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

91% identical to generate-tuned-profile — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

extensions/node-tuning/commands/generate-tuned-profile.md · 201 lines

How it starts

The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Name

node-tuning:generate-tuned-profile

Synopsis

/node-tuning:generate-tuned-profile [profile-name] [--summary TEXT] [--include VALUE ...] [--sysctl KEY=VALUE ...] [--match-label KEY[=VALUE] ...] [options]

Description

The node-tuning:generate-tuned-profile command streamlines creation of tuned.openshift.io/v1 manifests for the OpenShift Node Tuning Operator. It captures the desired Tuned profile metadata, tuned daemon configuration blocks (e.g. [sysctl], [variables], [bootloader]), and recommendation rules, then invokes the helper script at extensions/node-tuning/skills/scripts/generate_tuned_profile.py to render a ready-to-apply YAML file.

Use this command whenever you need to:

  • Bootstrap a new Tuned custom profile targeting selected nodes or machine config pools
  • Generate manifests that can be version-controlled alongside other automation
  • Iterate on sysctl, bootloader, or service parameters without hand-editing multi-line YAML

The generated manifest follows the structure expected by the cluster Node Tuning Operator:

apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
  name: <profile-name>
  namespace: openshift-cluster-node-tuning-operator
spec:
  profile:
  - data: |
      [main]
      summary=...
      include=...
      ...
    name: <profile-name>
  recommend:
  - machineConfigLabels: {...}
    match:
    - label: ...
      value: ...
    priority: <priority>
    profile: <profile-name>

Implementation

  1. Collect inputs
    • Confirm Python 3.8+ is available (python3 --version).
    • Gather the Tuned profile name, summary, optional include chain, sysctl values, variables, and any additional section lines (e.g. [bootloader], [service]).
    • Determine targeting rules: either --match-label entries (node labels) or --machine-config-label entries (MachineConfigPool selectors).
    • Decide whether an accompanying MachineConfigPool (MCP) workflow is required for kernel boot arguments (see Advanced Workflow below).
    • Use the helper's --list-nodes and --label-node flags when you need to inspect or label nodes prior to manifest generation.

Read the full file on GitHub · 201 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 201 lines · 21 tokens per session scan A 1b8a610dbec9

Subscribe to this mod's changes

generate-tuned-profile is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 21 tokens to every session and 2,337 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to generate-tuned-profile, differing in 12 lines, and is treated as a copy.