Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add llm-d-incubation/llm-d-skills --skill deploy-llm-dgit clone --depth 1 https://github.com/llm-d-incubation/llm-d-skillsWrote 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/skills/llm-d-incubation/llm-d-skills/deploy-llm-d)<a href="https://agentmods.dev/skills/llm-d-incubation/llm-d-skills/deploy-llm-d"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/deploy-llm-d/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/skills/llm-d-incubation/llm-d-skills/deploy-llm-d"><img src="https://agentmods.dev/badge/skills/llm-d-incubation/llm-d-skills/deploy-llm-d.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 | $0.00046 | $0.03906 |
| Opus 5 | $0.00023 | $0.01953 |
| Sonnet 5 | $0.00009 | $0.00781 |
| Haiku 4.5 | $0.00005 | $0.00391 |
Grade A, and why
deploy-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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy llm-d Stack
📋 Command Execution Notice
Before executing any command, I will:
- Explain what the command does - A clear description of the command's purpose and expected outcome
- Show the actual command - The exact command that will be executed
- Explain why it's needed - How this command fits into the overall deployment workflow
This ensures you understand each step before it happens and can verify the actions align with your intentions.
🔔 ALWAYS NOTIFY THE USER BEFORE CREATING ANYTHING
RULE: Before creating ANY resource — including namespaces, PVCs, files, Helm releases, HTTPRoutes, or any Kubernetes object — you MUST first tell the user what you are about to create and why. Critical rules to follow when deploying and managing llm-d:
-
Do NOT change cluster-level definitions All changes must be made exclusively inside the designated project namespace. Never modify cluster-wide resources (e.g., ClusterRoles, ClusterRoleBindings, StorageClasses, Nodes, or any resource outside the target namespace). Scope every
kubectl apply,helm install, andhelmfile applycommand to the target namespace using-n ${NAMESPACE}. -
Do NOT modify any existing code you did not create Only create new files and modify them as needed. Never edit pre-existing files in the repository (e.g., existing
values.yaml,helmfile.yaml,httproute.yaml,README.md, or any other committed file). If customization is required, create a new file (e.g.,values-custom.yaml,httproute-custom.yaml) and reference it instead.
Overview
llm-d provides Well-Lit Path deployment guides in the guides/ directory of ${LLMD_PATH}. The guide index at ${LLMD_PATH}/guides/README.md is the main reference for available deployment paths and supporting guides.
To discover available Well-Lit Paths, the agent will:
- Read
${LLMD_PATH}/guides/README.md - Extract the currently listed guides and descriptions
- Read the selected guide's README for guide-specific requirements
- Present the available guides to the user with their descriptions
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 428 lines · 46 tokens per session scan A 152139b74609
deploy-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 46 tokens to every session and 3,906 once invoked, about $0.0002 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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