Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/GoogleCloudPlatform/vertex-ai-samplesnpx agentmods add skills/googlecloudplatform/vertex-ai-samples/geminiWrote 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/vertex-ai-samples/gemini)<a href="https://agentmods.dev/skills/googlecloudplatform/vertex-ai-samples/gemini"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/vertex-ai-samples/gemini/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/vertex-ai-samples/gemini"><img src="https://agentmods.dev/badge/skills/googlecloudplatform/vertex-ai-samples/gemini.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.01640 |
| Opus 5.5 | $0.00000 | $0.00656 |
| Sonnet 5.5 | $0.00000 | $0.00328 |
| Haiku 4.5 | $0.00000 | $0.00164 |
Grade A, and why
gemini 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 1mo 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: vertex-tuning-gemini description: > Vertex AI Gemini Model Tuning. Use when you need to fine-tune Gemini models using Vertex AI's infrastructure.
Vertex AI Gemini Model Tuning
Overview
This skill provides procedural knowledge for fine-tuning Gemini Large Language Models using Vertex AI's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment.
Workflow Decision Tree
-
Environment Check: Has the environment (Auth, APIs, IAM, Venv) been initialized?
- No → Go to Phase 0: Environment & IAM Setup.
- Yes → Proceed.
-
Dataset Status: Is the dataset ready in JSONL format and uploaded to GCS?
- No → Go to Phase 1: Dataset Preparation & Upload.
- Yes → Proceed.
-
Configuration: Have the target Gemini model and hyperparameters been decided?
- No → Go to Phase 2: Model Configuration & Recommendation.
- Yes → Proceed.
-
Job Status: Has the tuning job been submitted?
- No → Go to Phase 3: Tuning Job Execution.
- Yes → Proceed.
-
Job Completion: Is the tuning job complete?
- No → Go to Phase 4: Monitoring.
- Yes → Proceed.
-
Deployment: Has the tuned model been deployed (if required)?
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.
- 1mo ago First seen · 208 lines · 0 tokens per session scan A 1769e8b6e60e
gemini is a skill published in the GitHub repository GoogleCloudPlatform/vertex-ai-samples (790 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,640 tokens. 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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