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-microsoft-discoverygit 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-microsoft-discovery)<a href="https://agentmods.dev/skills/microsoftdocs/agent-skills/azure-microsoft-discovery"><img src="https://agentmods.dev/badge/skills/microsoftdocs/agent-skills/azure-microsoft-discovery/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-microsoft-discovery"><img src="https://agentmods.dev/badge/skills/microsoftdocs/agent-skills/azure-microsoft-discovery.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 | $0.00078 | $0.02695 |
| Opus 5.5 | $0.00031 | $0.01078 |
| Sonnet 5.5 | $0.00016 | $0.00539 |
| Haiku 4.5 | $0.00008 | $0.00269 |
Grade A, and why
azure-microsoft-discovery 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 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 · -1 lines · -37 tokens per session 8842201e9637
- 14d ago Changed · +36 tokens per session 12c76131a391
- 21d ago Changed · +4 lines · -33 tokens per session e9dc9cea5b37
- 27d ago Changed · +36 tokens per session 806571bfcfe3
- 1mo ago Changed · +2 lines · -2 tokens per session ab8e5c72e22f
- 1mo ago Changed · +5 lines · +2 tokens per session 345eed3ab7a0
- 1mo ago First seen · 119 lines · 76 tokens per session scan A 2c8d5b54af60
azure-microsoft-discovery is a skill published in the GitHub repository MicrosoftDocs/Agent-Skills (787 stars, last pushed yesterday), licensed CC-BY-4.0. It adds 78 tokens to every session and 2,695 once invoked, about $0.0003 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…