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 ancoleman/ai-design-components --skill deploying-on-gcpgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/deploying-on-gcp)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/deploying-on-gcp"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/deploying-on-gcp/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/ancoleman/ai-design-components/deploying-on-gcp"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/deploying-on-gcp.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 | $0.00066 | $0.03534 |
| Opus 5 | $0.00033 | $0.01767 |
| Sonnet 5 | $0.00013 | $0.00707 |
| Haiku 4.5 | $0.00007 | $0.00353 |
Grade A, and why
deploying-on-gcp 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 9d 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 — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GCP Patterns
Build applications and infrastructure using Google Cloud Platform services with appropriate service selection, architecture patterns, and best practices.
Purpose
This skill provides decision frameworks and implementation patterns for Google Cloud Platform (GCP) services across compute, storage, databases, data analytics, machine learning, networking, and security. It guides service selection based on workload requirements and demonstrates production-ready patterns using Terraform, Python SDKs, and gcloud CLI.
When to Use
Use this skill when:
- Selecting GCP compute services (Cloud Run, GKE, Cloud Functions, Compute Engine, App Engine)
- Choosing storage or database services (Cloud Storage, Cloud SQL, Spanner, Firestore, Bigtable, BigQuery)
- Designing data analytics pipelines (BigQuery, Pub/Sub, Dataflow, Dataproc, Composer)
- Implementing ML workflows (Vertex AI, AutoML, pre-trained APIs)
- Architecting network infrastructure (VPC, Load Balancing, CDN, Cloud Armor)
- Setting up IAM, security, and cost optimization
- Migrating from AWS or Azure to GCP
- Building multi-cloud or GCP-first architectures
Core Concepts
GCP Service Categories
Compute Options:
- Cloud Run: Serverless containers for stateless HTTP services (auto-scale to zero)
- GKE (Google Kubernetes Engine): Managed Kubernetes for complex orchestration
- Cloud Functions: Event-driven functions for simple processing
- Compute Engine: Virtual machines for full OS control
- App Engine: Platform-as-a-Service for web applications
Storage & Databases:
- Cloud Storage: Object storage with Standard/Nearline/Coldline/Archive tiers
- Cloud SQL: Managed PostgreSQL/MySQL/SQL Server (up to 96TB)
- Cloud Spanner: Global distributed SQL with 99.999% SLA
- Firestore: NoSQL document database with real-time sync
- Bigtable: Wide-column NoSQL for time-series and IoT (petabyte scale)
- AlloyDB: PostgreSQL-compatible with 4x performance improvement
What ships with it
10 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.
- examples/gcloud/common-commands.sh 29 KB runs code
- examples/terraform/cloud-run-service.tf 8.6 KB
- outputs.yaml 9.9 KB
- references/compute-services.md 19 KB
- references/cost-optimization.md 11 KB
- references/data-analytics.md 11 KB
- references/ml-ai-services.md 9.1 KB
- references/networking.md 6.4 KB
- references/security-iam.md 9.1 KB
- references/storage-databases.md 10 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.
- 9d ago First seen · 433 lines · 66 tokens per session scan A 9d827a99f4ee
deploying-on-gcp is a skill published in the GitHub repository ancoleman/ai-design-components (519 stars, last pushed 9mo ago), licensed MIT. It adds 66 tokens to every session and 3,534 once invoked, about $0.0003 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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