Devops
nicepkg/auto-company
Deploy to Cloudflare (Workers, R2, D1), Docker, GCP (Cloud Run, GKE), Kubernetes (kubectl, Helm).
Agent skill
Gke Inference Stack Deploy
by GoogleCloudPlatform in GoogleCloudPlatform/accelerated-platforms
Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
Install Gke Inference Stack Deploy
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deploy --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gke-inference-stack-deploy .claude/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.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/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .claude/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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 install https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deployType 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.
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deploy --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/gke-inference-stack-deploy .agents/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .agents/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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.
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deploy --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/gke-inference-stack-deploy .cursor/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .cursor/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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 https://github.com/GoogleCloudPlatform/accelerated-platforms.git --path skills/gke-inference-stack-deploy--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deploy --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/gke-inference-stack-deploy .gemini/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.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/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .gemini/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deployInstalls 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).
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/gke-inference-stack-deploy .github/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.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/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .github/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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.
$ npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GoogleCloudPlatform/accelerated-platforms gke-inference-stack-deploy --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GoogleCloudPlatform/accelerated-platforms.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/gke-inference-stack-deploy .opencode/skills/gke-inference-stack-deploy && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gke-inference-stack-deploy" agent skill from https://github.com/GoogleCloudPlatform/accelerated-platforms/tree/main/skills/gke-inference-stack-deploy into .opencode/skills/gke-inference-stack-deploy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-inference-stack-deploy", 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
gke-inference-stack-deploy- GitHub stars
- 106
- Token cost
- ~1.1k tokens
- SKILL.md length
- 296 words
- Files
- 2
- Skills in repo
- 3
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
- Works in 5 steps: Prerequisites & Platform Configuration → Accelerator & Hardware Configuration → Deploy Inference Platform & Prerequisite… → …
- DevOps & Cloud work in your project
- SKILL.md covers 1. Prerequisites & Platform…, 2. Accelerator & Hardware…, 3. Deploy Inference Platform &… and 4. Deploy Inference…, plus 1 more section
- Calls terraform, kubectl and gcloud
What it does
Gke Inference Stack Deploy is an agent skill from GoogleCloudPlatform/accelerated-platforms. Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `evals/evals.json`).
It sits in DevOps & Cloud. It works with Google Kubernetes Engine and Google Cloud. The repository describes itself as: This repository is a collection of accelerated platform best practices, reference architectures, example use cases, reference implementations, and various other assets on Google… The licence is Apache-2.0.
When your agent uses it
- DevOps & Cloud work in your project
Example prompts
- “Use the gke-inference-stack-deploy skill to deploy the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads”
- “/gke-inference-stack-deploy”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): kubectl, gcloud, terraform, python3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit bab190a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
kubectlgcloudterraformpython3
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
terraformkubectlgcloud
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use kubectl and gcloud, which can reach the network depending on how they are called.
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
Gke Inference Stack Deploy loads about 1.1k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 296 words of instructions outside code blocks.
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 GoogleCloudPlatform/accelerated-platforms at commit bab190a, republished under its Apache-2.0 licence (© GoogleCloudPlatform). 296 words, ~1,066 tokens.
Download SKILL.mdSave it as
.claude/skills/gke-inference-stack-deploy/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- gke-inference-stack-deploy
- description
- Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
- allowed-tools
- kubectl, gcloud, terraform, python3
- version
- 0.1.0
Deploy GKE Inference Stack Skill
Follow these instructions to provision the GKE Base Platform and inference-specific terra-services.
1. Prerequisites & Platform Configuration
Ask the user (only for information not provided in the user's prompt):
- "What is the path to the repository?" -> Set ACP_REPO_DIR
- "What is your Google Cloud project ID?" -> Set platform_default_project_id
- "What GKE region would you like to use?" -> Set cluster_region
- "What platform name would you like to set?" -> Set platform_name
- "Would you like to deploy an Autopilot or Standard cluster?" -> Set cluster_type
Action: Inject these values into the appropriate
tfvarsfiles (${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvarsandcluster.auto.tfvars) usingsed.bash # Update platform variables sed -i 's/^platform_name.*/platform_name = "<platform_name>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars" grep -q "^platform_default_project_id" "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars" || echo "platform_default_project_id = \"\"" >> "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars" sed -i 's/^platform_default_project_id.*/platform_default_project_id = "<project_id>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/platform.auto.tfvars" # Update cluster variables sed -i 's/^cluster_region.*/cluster_region = "<cluster_region>"/g' "${ACP_REPO_DIR}/platforms/gke/base/_shared_config/cluster.auto.tfvars"
2. Accelerator & Hardware Configuration
Ask the user: "Which accelerator(s) would you like to deploy for inference? You can select GPU, TPU, or both."
- Options: GPU (
rtx-pro-6000,h100,h200), TPU (v6e), or both.
- Options: GPU (
Action:
- Determine if the target inference terra-services are
online_gpu,online_tpu, or both based on the chosen accelerator(s). - Update accelerator configurations in the shared
tfvarsif necessary based on user selection.
- Determine if the target inference terra-services are
3. Deploy Inference Platform & Prerequisite Terra-Services
Run the appropriate inference reference architecture deployment script based on the cluster type selected by the user. This provisions the core platform along with prerequisite inference services (such as Hugging Face and monitoring initialization). Always refer to existing scripts for execution rather than defining the CORE_TERRASERVICES_APPLY array explicitly.
- For Standard Cluster:
bash "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/deploy-standard.sh" - For Autopilot Cluster:
bash "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/deploy-ap.sh"
4. Deploy Inference Terra-Services
Navigate to the specific inference terra-service directories (online_gpu, online_tpu, or both) and execute Terraform commands.
# Depending on what the user chose, add "online_gpu" and/or "online_tpu" to this array
declare -a selected_terraservices=( "online_gpu" "online_tpu" )
for inference_terraservice in "${selected_terraservices[@]}"; do
cd "${ACP_REPO_DIR}/platforms/gke/base/use-cases/inference-ref-arch/terraform/${inference_terraservice}/"
terraform init
terraform plan -input=false -out=tfplan
terraform apply -input=false tfplan
rm tfplan
done5. Verification
Retrieve GKE cluster credentials and verify the deployment. Note: if using NAP (Node Auto-Provisioning), nodes may not be visible until workloads are deployed.
gcloud container clusters get-credentials "<platform_name>" --region "<cluster_region>" --project "<project_id>" --dns-endpoint
kubectl get computeclasses
kubectl get namespaces # You should see the online_gpu and/or online_tpu namespace depending on what was deployed© GoogleCloudPlatform, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/gke-inference-stack-deploy of GoogleCloudPlatform/accelerated-platforms.
- SKILL.md
- evals/evals.json
Open the folder on GitHubat commit bab190a
Compare with similar skills
Gke Inference Stack Deploy 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gke Inference Stack Deploy this skillGoogleCloudPlatform/accelerated-platforms | 106 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Devopsnicepkg/auto-company | 192 | 2 repos | ~814 | Automated safety check: Pass | MIT | |
| Kcli Cluster Deploymentkarmab/kcli | 653 | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Google Agents CLI Publishpifferologo/cloud-agents-cli | 129 | 1 repos | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| DeployingGoogleCloudPlatform/race-condition | 234 | — | ~3k | Automated safety check: Pass | Custom licence | |
| Aicr Uat ReportNVIDIA/aicr | 439 | — | ~3.2k | Automated safety check: Pass | Apache-2.0 |
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Questions about Gke Inference Stack Deploy
What does Gke Inference Stack Deploy do?
Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads. Gke Inference Stack Deploy is an agent skill from GoogleCloudPlatform/accelerated-platforms. Deploys the GKE base platform and inference-specific terra-services (GPU/TPU) for accelerated workloads.
When should I use Gke Inference Stack Deploy?
Gke Inference Stack Deploy fits situations like: devOps & Cloud work in your project.
How do I install Gke Inference Stack Deploy in Claude Code?
Run `npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a claude-code`. Or copy the skill folder (skills/gke-inference-stack-deploy in GoogleCloudPlatform/accelerated-platforms) into .claude/skills/gke-inference-stack-deploy in your project. Claude Code loads it when a task matches its description.
How do I install Gke Inference Stack Deploy in Codex?
Run `npx skills add GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a codex`. Or copy the skill folder (skills/gke-inference-stack-deploy in GoogleCloudPlatform/accelerated-platforms) into .agents/skills/gke-inference-stack-deploy in your project. Codex loads it when a task matches its description.
Can I use Gke Inference Stack Deploy 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 GoogleCloudPlatform/accelerated-platforms --skill gke-inference-stack-deploy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gke-inference-stack-deploy, .gemini/skills/gke-inference-stack-deploy, .github/skills/gke-inference-stack-deploy and .opencode/skills/gke-inference-stack-deploy in your project.
What does Gke Inference Stack Deploy need to run?
Going by SKILL.md and its folder, Gke Inference Stack Deploy needs the command-line tools its instructions call (terraform, kubectl and gcloud). Our summary lists: Python 3. Its frontmatter pre-approves these tools: kubectl, gcloud, terraform, python3.
Does Gke Inference Stack Deploy 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 Gke Inference Stack Deploy 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 Gke Inference Stack Deploy use?
Gke Inference Stack Deploy is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Gke Inference Stack Deploy use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 Gke Inference Stack Deploy?
Skills that share tags, products or a category with Gke Inference Stack Deploy: Devops (nicepkg/auto-company, 192 stars), Kcli Cluster Deployment (karmab/kcli, 653 stars), Google Agents CLI Publish (pifferologo/cloud-agents-cli, 129 stars) and Deploying (GoogleCloudPlatform/race-condition, 234 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gke Inference Stack Deploy?
GoogleCloudPlatform (a GitHub organization) maintains it in GoogleCloudPlatform/accelerated-platforms, which has 106 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 5, 2026.
Source: GoogleCloudPlatform/accelerated-platforms on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.