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 gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-pipeline-resource-provisioninggit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote 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/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-pipeline-resource-provisioning)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-pipeline-resource-provisioning"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-pipeline-resource-provisioning/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/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-pipeline-resource-provisioning"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-pipeline-resource-provisioning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00191 | $0.01485 |
| Opus 5 | $0.00096 | $0.00743 |
| Sonnet 5 | $0.00038 | $0.00297 |
| Haiku 4.5 | $0.00019 | $0.00148 |
Grade A, and why
gcp-pipeline-resource-provisioning 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gcp-pipeline-resource-provisioning — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How to use this skill
Create or update existing deployment.yaml file and deploy resources. All
configuration files MUST be maintained together in the repository root.
Mandatory labels
[!IMPORTANT]
Whenever you generate resource definitions in
deployment.yaml, you MUST directly populate thedatacloudlabel underdefinition.labelsfor every resource to track the source of creation. Determine the value based on your current IDE environment:
- For Antigravity, set
datacloud: "antigravity"- For VS Code, set
datacloud: "vscode"- For any other environment, set
datacloud: "other"Do not use a variable substitution for this label; hardcode the appropriate string value directly into each resource definition (e.g., replacing
__REQUIRED_LABEL__placeholders).Special rule for BigQuery DTS Ingestion: Whenever you generate a
bigquerydatatransfer.transferConfigindeployment.yaml, you MUST also explicitly define its target destinationbigquery.datasetin the same file and apply thedatacloudlabel to it. You must do this even if the dataset already exists, to ensure the destination dataset's labels are patched and updated.
Step 1: Supported Resource Types
The framework supports deploying various GCP resources. To see the comprehensive list of supported resource types, run the following command:
gcloud beta orchestration-pipelines resource-types list
Refer to: references/gcp-pipeline-resource-provisioning_spec.md to understand
the template for deployment.yaml.
Step 2: Discover Environment Parameters
Before generating configurations, discover the actual values for the target project, region, environment, and commit SHA.
[!TIP]
If
deployment.yamlalready exists in the repository root, prioritize extractingprojectandregionfrom the target environment configuration (e.g.,dev).
-
Project ID:
gcloud config get project
What ships with it
1 file 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.
- 10d ago First seen · 172 lines · 191 tokens per session scan A 375d3a13d9fa
gcp-pipeline-resource-provisioning is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (180 stars, last pushed yesterday), licensed Apache-2.0. It adds 191 tokens to every session and 1,485 once invoked, about $0.0010 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 2026-08-30.
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