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.
git clone --depth 1 https://github.com/wangke19/gemini-ai-helpersnpx agentmods add commands/wangke19/gemini-ai-helpers/generate-tuned-profileWrote 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.
[](https://agentmods.dev/commands/wangke19/gemini-ai-helpers/generate-tuned-profile)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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
- 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-labelentries (node labels) or--machine-config-labelentries (MachineConfigPool selectors). - Decide whether an accompanying MachineConfigPool (MCP) workflow is required for kernel boot arguments (see Advanced Workflow below).
- Use the helper's
--list-nodesand--label-nodeflags when you need to inspect or label nodes prior to manifest generation.
- Confirm Python 3.8+ is available (
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.
- 8d ago First seen · 201 lines · 21 tokens per session scan A 1b8a610dbec9
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.
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