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 MicrosoftDocs/Agent-Skills --skill azure-databricksgit clone --depth 1 https://github.com/MicrosoftDocs/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/microsoftdocs/agent-skills/azure-databricks)<a href="https://agentmods.dev/skills/microsoftdocs/agent-skills/azure-databricks"><img src="https://agentmods.dev/badge/skills/microsoftdocs/agent-skills/azure-databricks/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/microsoftdocs/agent-skills/azure-databricks"><img src="https://agentmods.dev/badge/skills/microsoftdocs/agent-skills/azure-databricks.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 | $0.00128 | $0.13112 |
| Opus 5.5 | $0.00051 | $0.05245 |
| Sonnet 5.5 | $0.00026 | $0.02622 |
| Haiku 4.5 | $0.00013 | $0.01311 |
Grade A, and why
azure-databricks 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 today.
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.
The source is not reproduced here
Licensed CC-BY-4.0
The repository is licensed CC-BY-4.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
7 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.
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.
- today Changed · +2 lines · +4 tokens per session 8a89f3b53470
- 14d ago Changed · +10 lines · -2 tokens per session 1e92802a161a
- 21d ago Changed bbc05ecc3220
- 27d ago Changed · -2 lines · +2 tokens per session c2f99838490a
- 1mo ago Changed · -98 lines 727ea7a24d6e
- 1mo ago Changed · -4 lines 77189193b7f6
- 1mo ago First seen · 492 lines · 124 tokens per session scan A 19440c5e02eb
azure-databricks is a skill published in the GitHub repository MicrosoftDocs/Agent-Skills (787 stars, last pushed yesterday), licensed CC-BY-4.0. It adds 128 tokens to every session and 13,112 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
azure-mgmt-weightsandbiases-dotnet
Azure Resource Manager SDK for deploying and managing Weights & Biases ML experiment tracking instances via Azure Marketplace.
litellm-vertex-gemini-local-gateway
Build a local macOS LiteLLM gateway that exposes Google Cloud Vertex AI Gemini behind an Anthropic-compatible endpoint, then connect Claude Code and OpenClaw to it without breaking existing setups. Use when starting from a fresh machine, when you need a self-starting LaunchAgent service on 127.0.0.1, when Claude Code…
serving-llms-vllm
Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.
data-pipeline
Professional Data Pipeline Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…