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 JosiahSiegel/claude-plugin-marketplace --skill azure-ml-foundry-workspacegit clone --depth 1 https://github.com/JosiahSiegel/claude-plugin-marketplaceWrote 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/josiahsiegel/claude-plugin-marketplace/azure-ml-foundry-workspace)<a href="https://agentmods.dev/skills/josiahsiegel/claude-plugin-marketplace/azure-ml-foundry-workspace"><img src="https://agentmods.dev/badge/skills/josiahsiegel/claude-plugin-marketplace/azure-ml-foundry-workspace/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/josiahsiegel/claude-plugin-marketplace/azure-ml-foundry-workspace"><img src="https://agentmods.dev/badge/skills/josiahsiegel/claude-plugin-marketplace/azure-ml-foundry-workspace.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 | $0.00191 | $0.03937 |
| Opus 5 | $0.00096 | $0.01969 |
| Sonnet 5 | $0.00038 | $0.00787 |
| Haiku 4.5 | $0.00019 | $0.00394 |
Grade A, and why
azure-ml-foundry-workspace 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 13d 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Machine Learning Workspace / Azure AI Foundry - Complete Deep-Dive Reference
Authoritative reference for every aspect of Azure Machine Learning Workspace (Azure AI Foundry) including architecture, networking, private endpoints, compute clusters, endpoint deployment, managed identities, ACR integration, storage accounts, all CLI and PowerShell commands, log reading, debugging, and Terraform integration.
1. ARCHITECTURE AND CORE CONCEPTS
Workspace Resource Hierarchy
Azure Subscription
└── Resource Group
├── Azure ML Workspace (Microsoft.MachineLearningServices/workspaces)
│ ├── Dependent Resources (auto-created or BYO)
│ │ ├── Azure Storage Account (default datastore)
│ │ ├── Azure Key Vault (secrets, connection strings)
│ │ ├── Azure Application Insights (telemetry)
│ │ └── Azure Container Registry (Docker images for environments)
│ ├── Compute Targets
│ │ ├── Compute Instances (dev/test VMs)
│ │ ├── Compute Clusters (AmlCompute - training)
│ │ ├── Serverless Compute (on-demand)
│ │ ├── Kubernetes Compute (AKS / Arc-enabled)
│ │ └── Attached Compute (Databricks, HDInsight, VMs)
│ ├── Data Assets (versioned references to data)
│ ├── Datastores (connections to storage)
│ ├── Environments (Docker + conda specs)
│ ├── Models (registered trained models)
│ ├── Endpoints
│ │ ├── Managed Online Endpoints (real-time)
│ │ ├── Kubernetes Online Endpoints (BYO infra)
│ │ ├── Batch Endpoints (large-scale scoring)
│ │ └── Serverless Endpoints (MaaS - pay-per-token)
│ ├── Jobs (training runs, pipelines, sweeps)
│ ├── Components (reusable pipeline steps)
│ ├── Schedules (recurring job triggers)
│ └── Registries (cross-workspace sharing)
└── AI Foundry Hub (kind=hub) + Projects (kind=project)
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.
- 13d ago First seen · 347 lines · 191 tokens per session scan A d89e7f02f88d
azure-ml-foundry-workspace is a skill published in the GitHub repository JosiahSiegel/claude-plugin-marketplace (54 stars, last pushed 2mo ago), licensed MIT. It adds 191 tokens to every session and 3,937 once invoked, about $0.0010 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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