configure-wva-autoscaling-llm-d

configure-wva-autoscaling-llm-d is a skill for Claude Code, Codex from llm-d-incubation/llm-d-skills. It costs 68 tokens per session (7,113 once invoked), scanned A, original, Apache-2.0.

A guided setup for Workload Variant Autoscaler, a Kubernetes component that adjusts inference deployment capacity for large language models. Kubernetes is software for running and managing containerized applications.

In plain words
What is it for?
Use it to choose a Kubernetes namespace, find the relevant llm-d decode deployments, configure scaling settings, deploy WVA with Make and kubectl, and verify each command.
Why use it?
It reduces configuration mistakes by requiring choices and verification at each deployment step.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is WVA_NS=$WVA_NS ./deploy/install.sh --undeploy.

Good fit Use it to choose a Kubernetes namespace, find the relevant llm-d decode deployments, configure scaling settings, deploy WVA with Make and kubectl, and verify each command.

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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/llm-d-incubation/llm-d-skills
agentmods
npx agentmods add skills/llm-d-incubation/llm-d-skills/configure-wva-autoscaling-llm-d

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin configure-wva-autoscaling-llm-d/plugin install configure-wva-autoscaling-llm-d after adding the marketplace above.

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 configure-wva-autoscaling-llm-d

README.md
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<a href="https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/configure-wva-autoscaling-llm-d"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/configure-wva-autoscaling-llm-d.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,113 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 original No closer match found 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 $0.00068 $0.07113
Opus 5 $0.00034 $0.03556
Sonnet 5 $0.00014 $0.01423
Haiku 4.5 $0.00007 $0.00711

Measured 12d ago against content hash be52135acf06, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

configure-wva-autoscaling-llm-d 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 12d ago.

The scan reads SKILL.md. This mod also ships 8 executable files (scripts/apply-hpa.sh, scripts/detect-accelerator.sh, scripts/generate-deploy-script.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/configure-wva-autoscaling-llm-d/SKILL.md · 642 lines

How it starts

The opening of the file, as written. The whole thing — 642 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Behavior Rules

  1. Follow steps IN ORDER. Never skip or combine steps.
  2. STOP after each step and ask for explicit permission to proceed to the next step.
  3. Do NOT modify existing repository code. Cloning a missing repo is allowed. Exception: the kustomize symlink fix in Step 4b is a known bug fix — apply it if needed.
  4. Use existing skill scripts when possible — see scripts/SCRIPTS.md.
  5. Before creating any Kubernetes resource, state what will be created and why.
  6. After each kubectl/make command, run a verification check and report the result before continuing.

Step 1 — Select Target Namespace and Deployments

Ask the user:

"Which Kubernetes namespace should WVA monitor?" (Provide a single namespace, e.g., my-llm-ns)

Export the answer:

export WVA_NS=<namespace>

WVA will be deployed into this namespace so it can watch the llm-d workloads there.

Then discover ALL llm-d decode deployments in that namespace:

kubectl get deployment -n $WVA_NS -l llm-d.ai/role=decode -o custom-columns=NAME:.metadata.name,MODEL:.metadata.labels.llm-d\.ai/model-id,REPLICAS:.spec.replicas

If no results, try the alternative label:

kubectl get deployment -n $WVA_NS -l app.kubernetes.io/part-of=llm-d -o custom-columns=NAME:.metadata.name,REPLICAS:.spec.replicas

Also discover EPPs (InferencePool / EndpointPickerPool) and map which deployments each EPP routes to:

kubectl get inferencepool -n $WVA_NS -o custom-columns=NAME:.metadata.name,SELECTOR:.spec.targetPortNumber 2>/dev/null || \
kubectl get endpointpickerpool -n $WVA_NS -o yaml 2>/dev/null

Determine which EPP routes to which decode deployments by inspecting the EPP's selector labels and matching them against the deployments' labels. Deployments served by the same EPP share a single WVA saturation-scaling ConfigMap — their thresholds (kv_cache_threshold, queue_length_threshold, etc.) must be identical. Deployments under different EPPs can have independent thresholds.

Read the full file on GitHub · 642 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. 12d ago First seen · 642 lines · 68 tokens per session scan A be52135acf06

Subscribe to this mod's changes

configure-wva-autoscaling-llm-d is a skill published in the GitHub repository llm-d-incubation/llm-d-skills (6 stars, last pushed 29d ago), licensed Apache-2.0. It adds 68 tokens to every session and 7,113 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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