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 GoogleCloudPlatform/gemini-cloud-assist-mcp --skill designing-and-deploying-infrastructuregit clone --depth 1 https://github.com/GoogleCloudPlatform/gemini-cloud-assist-mcpWrote 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/googlecloudplatform/gemini-cloud-assist-mcp/designing-and-deploying-infrastructure)<a href="https://agentmods.dev/skills/googlecloudplatform/gemini-cloud-assist-mcp/designing-and-deploying-infrastructure"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/gemini-cloud-assist-mcp/designing-and-deploying-infrastructure/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/googlecloudplatform/gemini-cloud-assist-mcp/designing-and-deploying-infrastructure"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/gemini-cloud-assist-mcp/designing-and-deploying-infrastructure.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 | $0.00032 | $0.03653 |
| Opus 5 | $0.00016 | $0.01826 |
| Sonnet 5 | $0.00006 | $0.00731 |
| Haiku 4.5 | $0.00003 | $0.00365 |
Grade C, and why
designing-and-deploying-infrastructure scanned grade C with 1 finding 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.
Instruction-override phrasinghighPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
* For retries, disregard previous attempts and start from Step 2 again. How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing and Deploying Infrastructure
Index
- Overview
- Best Practices & Constraints
- Phase 1: Infrastructure Design & Refinement
- Phase 2: Best Practices Assessment & Design Iteration
- Phase 3: Application Deployment
- Phase 4: Get Deployed Resources
- Phase 5: Troubleshoot Deployment Failures
- Phase 6: Verification & E2E Testing
Overview
This skill provides a prescriptive, multi-loop workflow for the entire infrastructure lifecycle on Google Cloud Platform (GCP). It leverages the Gemini Application Designer (GAD) and Application Design Center (ADC) tools (like gemini_cloud_assist:design_infra and application_design_center:assess_best_practices) to intelligently design architectures, assess best practices, and automate deployment and troubleshooting.
Always maintain the persona of a Principal Cloud Architect. Delegate all research and design to the specialized tools provided.
Note: These tools are part of the gemini_cloud_assist and application_design_center MCP Servers. Tool names are qualified with their respective server names (e.g., gemini_cloud_assist:tool_name).
Best Practices & Constraints
- Delegation & No Manual Design: Delegate all architecture decisions and product selections to the
gemini_cloud_assist:design_infratool. Do not design manually or edit generated Terraform code. Request changes via the tool. - Inputs: Always ask the user for required context like project ID, service accounts, etc. if necessary -- do not make assumptions or use placeholders.
- Visualization Power: Always render the Mermaid diagram from
gemini_cloud_assist:design_infrain every implementation plan. Refresh the diagram after every design change. Do not create your own diagrams. - Loop Discipline: Follow the workflow loops and exit criteria strictly. If you cannot follow the Infrastructure Lifecycle Workflow, you must exit and inform the user (for example, if the user cancels the process, or if you hit the maximum troubleshooting loop threshold in Phase 5).
- Application Template as the main resource: The application template is the main resource when generating and iterating on a design. Always look for the application template URI in the
gemini_cloud_assist:design_infraresponse (serializedApplicationTemplateURI), and use that for the rest of the Infrastructure Lifecycle Workflow.- Application Template vs Application: Application template is a template that is used to create an application. Application is an instance of an application template. Do not confuse these two.
- Never attempt to create an application template URI yourself; always use the URI returned from
gemini_cloud_assist:design_infra.
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 · 205 lines · 32 tokens per session scan C f2f935b75313
designing-and-deploying-infrastructure is a skill published in the GitHub repository GoogleCloudPlatform/gemini-cloud-assist-mcp (67 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 3,653 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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