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 G1Joshi/Agent-Skills --skill gcpgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/gcp)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/gcp"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/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/g1joshi/agent-skills/gcp"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/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 | $0.00023 | $0.00342 |
| Opus 5 | $0.00012 | $0.00171 |
| Sonnet 5 | $0.00005 | $0.00068 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
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 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.
What it actually says
Google Cloud Platform (GCP)
GCP is known for its data analytics (BigQuery) and being the home of Kubernetes. 2025 highlights Vertex AI for rapid model deployment and Cloud Run for serverless everywhere.
When to Use
- Big Data: BigQuery is arguably the best data warehouse for speed and ease of use.
- Kubernetes: GKE (Google Kubernetes Engine) often leads in features and stability (Autopilot).
- AI: TPU access and Vertex AI for training large models.
Core Concepts
Projects
The primary unit of isolation. Resources belong to a Project. Projects belong to Folders/Organization.
Global VPC
Unlike AWS/Azure, GCP VPCs are global. Subnets are regional. A single VPC can span the world.
IAM
Permissions are granted to Members (Users/Service Accounts) on Resources via Roles.
Best Practices (2025)
Do:
- Use Cloud Run: The default computed choice for stateless containers. Scales to zero, fast startup.
- Use Workload Identity: Let GKE workloads impersonate Service Accounts securely.
- Shared VPC: For orgs, use a Shared VPC in a Host Project to centralize networking.
Don't:
- Don't use
ownerrole: It's too broad. Use precise roles (storage.objectViewer).
References
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 · 45 lines · 23 tokens per session scan A 20658fc9e65d
gcp is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 23 tokens to every session and 342 once invoked, about $0.0001 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 2026-09-03.
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Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
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Speech-to-text via 9Router /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.