discover-azure-skills
Searches the Azure skills catalog and recommends installable agent skills by matching an Azure task to skill metadata and plugin installation guidance. WHEN: before starting any task that involves an Azure or Microsoft-cloud service, product, or data source, when no currently loaded skill or tool already covers it.
- License 许可
- MIT
- 最近更新
- 2026-11-14
- Stars
- ★ 1.6k
- Forks
- 267
01 前言 Front Matter
- name
- discover-azure-skills
- license
- MIT
- metadata
- { "author": "Microsoft", "version": "1.0.2" }
- description
- Searches the Azure skills catalog and recommends installable agent skills by matching an Azure task to skill metadata and plugin installation guidance. WHEN: before starting any task that involves an Azure or Microsoft-cloud service, product, or data source, when no currently loaded skill or tool already covers it.
02 资源文件 3
- 参考文档references/install/claude-code.md339 B
- 参考文档references/install/copilot-cli.md339 B
- 参考文档references/install/other.md328 B
03 SKILL.md 正文
Follow these steps to discover the available azure skill matching the given task description.
- List plugins
List Azure plugin directories from the GitHub Contents API: https://api.github.com/repos/microsoft/azure-skills/contents/.github/plugins?ref=main
In the result, each entry whose type is dir is a plugin directory. Skills are organized by plugins.
- List skills
For each plugin, list their skills from the GitHub Contents API: https://api.github.com/repos/microsoft/azure-skills/contents/.github/plugins/{plugin-dirname}/skills?ref=main
In the result, each entry whose type is dir is a skill directory. Each skill has a SKILL.md file that explains what this skill should be used for.
- Discover relevant skills
Eliminate the skills that obviously aren't relevant by their names. Then for each remaining skill, read their description from the API: https://raw.githubusercontent.com/microsoft/azure-skills/main/.github/plugins/{plugin-dirname}/skills/{skill-name}/SKILL.md
Use the descriptions to further eliminate skills that aren't relevant.
- Discover the plugin name of the relevant skills
For each relevant skill, discover their plugin name by reading the plugin.json from the GitHub Contents API: https://raw.githubusercontent.com/microsoft/azure-skills/main/.github/plugins/{plugin-dirname}/.plugin/plugin.json
This is important because a plugin's name may be different from its directory name. The installation commands depend on the plugin's name.
- Report the matched skills
Report the matched skills and offer instructions to install them. Skills can be installed by installing their plugin. Read the installation instructions matching the agent client to offer the installation instructions.
04 同仓库更多 Skills
python-appservice-deploy
Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
preset
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
customize
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
capacity
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
deploy-model
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).