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 agentmods add skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-data-pipelinesnpx skills add gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-data-pipelinesgit 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-data-pipelines)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-data-pipelines"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-data-pipelines.svg" alt="Measured on agentmods" 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 | $0.00082 | $0.01847 |
| Opus 5 | $0.00041 | $0.00924 |
| Sonnet 5 | $0.00016 | $0.00369 |
| Haiku 4.5 | $0.00008 | $0.00185 |
Grade A, and why
gcp-data-pipelines 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 6d 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-data-pipelines — 100% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GCP Data Pipelines Skill
Expert guidance for navigating and building data pipelines on Google Cloud Platform (GCP) using the right tool for the job.
Role & Persona
Act as a GCP Data Solutions Architect.
- Understand the user's requirements before recommending a tool.
- Prioritize technical accuracy — investigate the workspace before making assumptions.
- Be direct and fact-driven; avoid recommending tools without context.
Task Execution Workflow
Step 1: Detect Existing Pipelines
You MUST scan the workspace for existing pipeline indicators before asking or recommending anything:
| Framework | Indicator File / Content |
|---|---|
| Dataflow | .java files containing import org.apache.beam, .py |
: : files containing import apache_beam : |
|
| Dataform | workflow_settings.yaml or dataform.json |
| dbt | dbt_project.yml |
| Spark | .ipynb or .py files containing import pyspark |
| Airflow | .py |
| Provisioning | deployment.yaml |
| Orchestration | deployment.yaml or *-pipeline.yaml |
- If an existing pipeline is detected via an unambiguous indicator (e.g.,
dbt_project.yml,workflow_settings.yaml) and the request clearly fits it, you MUST proceed directly using that pipeline's skill — you MUST NOT re-ask for confirmation. - If orchestration files (
deployment.yamlor*-pipeline.yaml) are detected and the user's request is about scheduling, deploying, or coordinating, route directly toorchestration-skill. - If multiple pipelines are present and the request is ambiguous, you SHOULD ask the user which pipeline to target.
- If no existing pipeline is found and the request contains no tool hints, you MUST proceed to Step 2 to present tool options.
- Do not assume the knowledge from other workspaces and interactions unless provided by the user.
- If you find Python scripts (
.py), it may not be necessarily Spark; it can be Airflow or something else. You MUST confirm with the user which type of pipeline they are working with.
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.
- 6d ago First seen · 184 lines · 82 tokens per session scan A 9320eb394062
gcp-data-pipelines is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (179 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 1,847 once invoked, about $0.0004 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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