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-dataflownpx skills add gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-dataflowgit 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-dataflow)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-dataflow"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-dataflow.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 | $0.00207 | $0.04764 |
| Opus 5 | $0.00103 | $0.02382 |
| Sonnet 5 | $0.00041 | $0.00953 |
| Haiku 4.5 | $0.00021 | $0.00476 |
Grade A, and why
gcp-dataflow 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 5d 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 — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apache Beam Pipelines on Cloud Dataflow
Pipeline authoring
Use this section when implementing Dataflow pipeline logic using Apache Beam.
Check if existing Google Dataflow Template exists
Google provides a variety of pre-built, open source Dataflow templates that can be used for common scenarios. Before implementing a pipeline from scratch, you MUST follow the steps below to check whether a Dataflow template for the pipeline logic you need to implement already exists.
-
Step 1: Check for a matching Google Dataflow Template
- Identify the source and sink (e.g., GCS to BigQuery) from the user's request. Note: You MUST NOT proceed until the source and sink are clearly identified.
- Action: List templates in the public
dataflow-templatesbucket:- For Classic templates, check
gs://dataflow-templates/latest. - For Flex templates, check
gs://dataflow-templates/latest/flex. Usegcloud storage lsto list the contents.
- For Classic templates, check
- Match templates by name or description to the source and sink.
- If no matching template is found, go to Create a new pipeline from scratch.
-
Step 2: Confirm template selection
- Present the matched template(s) to the user with a brief explanation of why they match, and make a note of whether it is a Classic or Flex template.
- Action: Ask the user for explicit confirmation to proceed with this template.
- If the user rejects or prefers a custom solution, proceed to Create a new pipeline from scratch.
Create a new pipeline from scratch
Use this section when creating a new project for a Dataflow pipeline from scratch.
- If the user doesn't say explicitly which language (Java, Python, Go) shall be used to write the pipeline, you MUST confirm the language.
- Determine which version of Beam SDK should be used by searching for the most
recently released version of Apache Beam, unless the user already uses a
particular version.
- Action: Run a web search for the latest Apache Beam SDK release.
- YOU MUST use same version of Apache Beam consistently throughout the project
in Dockerfiles,
requirements.txt, and other similar files where versions are specified.
What ships with it
9 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.
- references/bottlenecks_and_parallelism_context.md 5.0 KB
- references/dataflow_diagnostics_reference.md 3.4 KB
- references/dataflow_metrics_bigquery.md 447 B
- references/dataflow_metrics_core_job.md 6.8 KB
- references/dataflow_metrics_pubsub.md 3.3 KB
- references/dataflow_metrics_streaming_engine.md 5.0 KB
- references/python_flex_template_reference.md 1.9 KB
- references/streaming_horizontal_autoscaling_analysis.md 4.6 KB
- references/streaming_job_health.md 2.7 KB
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.
- 5d ago First seen · 419 lines · 207 tokens per session scan A 85a57595c1b0
gcp-dataflow is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (178 stars, last pushed yesterday), licensed Apache-2.0. It adds 207 tokens to every session and 4,764 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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