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/googlecloudplatform/vertex-ai-samples/open-modelnpx skills add GoogleCloudPlatform/vertex-ai-samples --skill open-modelgit clone --depth 1 https://github.com/GoogleCloudPlatform/vertex-ai-samplesWhat 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 | $0.00000 | $0.02132 |
| Opus 5 | $0.00000 | $0.01066 |
| Sonnet 5 | $0.00000 | $0.00426 |
| Haiku 4.5 | $0.00000 | $0.00213 |
Grade A, and why
open-model 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 2d 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 — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: vertex-tuning-open-model description: > Vertex AI Open Model Tuning. Use when you need to fine-tune open models using Vertex AI's infrastructure.
Vertex AI Open Model Tuning
Overview
This skill provides procedural knowledge for fine-tuning Open Large Language Models (LLMs) 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 open 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 ships with it
8 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/data_prep.md 2.2 KB
- references/models.md 3.2 KB
- references/requirements.txt 99 B
- references/tuning_guide.md 2.7 KB
- scripts/calculate_cost.py 5.3 KB runs code
- scripts/deploy_open_model.py 2.0 KB runs code
- scripts/prepare_dataset.py 8.2 KB runs code
- scripts/tune_open_model.py 2.8 KB runs code
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.
- 2d ago First seen · 262 lines · 0 tokens per session scan A b1e7b2827139
open-model is a skill published in the GitHub repository GoogleCloudPlatform/vertex-ai-samples (780 stars, last pushed 7d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,132 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.
Other skills, from other repositories
dnanexus-integration
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
latchbio-integration
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
byted-emr-skills
“查询EMR集群的服务列表” “重启Spark服务” “获取 EMR 作业日志” “查看EMR serverless队列列表” “查看EMR serverless队列详情” “使用 EMR Serverless 运行任务” “在 EMR 上提交一个 Spark 作业” “帮我诊断一下失败的 Spark 作业” “分析一下EMR作业失败的原因”.
tos-file-access
Upload files or directories to TOS-compatible object storage for Volcano Engine or BytePlus and download files from URLs. Use this skill when (1) Upload Agent-generated files or directories for sharing, (2) Download files from URLs before Agent processing.
apim-terraform
Guide for creating Terraform files for Azure API Management (APIM) and related Azure services. Use when users want to create, modify, or understand Terraform configurations for APIM instances, APIs, backends, subscriptions, policies, products, loggers, diagnostics, and supporting infrastructure using the azurerm…
apim-bicep
Guide for building Bicep files for Azure API Management (APIM) and related Azure services. Use when users want to create, modify, or understand Bicep templates for APIM instances, APIs, backends, subscriptions, policies, products, loggers, diagnostics, and MCP servers. This skill provides Bicep syntax, patterns from…