azure-compute
**ANALYSIS SKILL** — Recommend Azure VM sizes and VMSS for workload, performance, and budget. Uses public docs and the Azure Retail Prices API. WHEN: "recommend VM size", "choose Azure VM", "GPU VM", "compare VM sizes", "VMSS vs VM", "autoscale VMs". DO NOT USE FOR: provisioning VMs (azure-prepare), VM pricing for budgets (azure-pricing MCP).
git clone --depth 1 https://github.com/jonathan-vella/apex /tmp/azure-compute && cp -r /tmp/azure-compute/.github/skills/azure-compute ~/.claude/skills/azure-computeSKILL.md
# Azure Compute Skill Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations by analyzing workload type, performance requirements, scaling needs, and budget. No Azure subscription required — all data comes from public Microsoft documentation and the unauthenticated Retail Prices API. ## When to Use This Skill - User asks which Azure VM or VMSS to choose for a workload - User needs VM size recommendations for web, database, ML, batch, HPC, or other workloads - User wants to compare VM families, sizes, or pricing tiers - User asks about trade-offs between VM options (cost vs performance) - User needs a cost estimate for Azure VMs without an Azure account - User asks whether to use a single VM or a scale set - User needs autoscaling, high availability, or load-balanced VM recommendations - User asks about VMSS orchestration modes (Flexible vs Uniform) ## Rules - **Always verify against live docs** — call `web_fetch` against `learn.microsoft.com` before finalizing recommendations; warn the user when `web_fetch` fails - **Default to General Purpose D-series** when workload type is unclear - **Default region** follows the canonical declaration in [copilot-instructions.md](../../copilot-instructions.md#azure-defaults-canonical); prices vary by region - **Default to single VM** when scaling needs are unclear; recommend VMSS only when autoscale, fleet, or mixed-size requirements are explicit - **VMSS pricing** = VM pricing × instance count (no extra VMSS charge) - **Reservation pricing** is recommended for long-lived production VMs (1y/3y commitments) - **No deployment** — this skill recommends sizes; for provisioning use `azure-prepare` ## Steps The full 6-step procedure (with all decision tables, dichotomy tree, and `web_fetch` URLs) lives in **[references/recommendation-workflow.md](references/recommendation-workflow.md)**. Load it on demand. Summary: 1. **Gather requirements** — workload type, vCPU/RAM, GPU, storage, budget, OS, region, instance count, scaling, HA, load balancing 2. **Determine VM vs VMSS** — VMSS for autoscale / fleet / mixed sizes (Flexible orchestration); VM for single long-lived servers, jumpboxes, AD DCs. Default to single VM when unsure 3. **Select VM family** — pick 2–3 candidates from [vm-families.md](references/vm-families.md), then verify specs via `web_fetch` against `learn.microsoft.com` 4. **Look up pricing** — Azure Retail Prices API per [retail-prices-api.md](references/retail-prices-api.md); for VMSS multiply by instance count 5. **Present 2–3 recommendations** — include hosting model, VM size, vCPU/RAM, instance count, $/hr, fit, trade-off 6. **Offer next steps** — reservation pricing, [Azure Pricing Calculator](https://azure.microsoft.com/pricing/calculator/), VMSS autoscale + networking docs > **Critical**: always verify recommendations against live `learn.microsoft.com` docs via `web_fetch`. If `web_fetch` fails, proceed with reference-file guidance and warn the user data may be stale. ## Error Handling | Scenario | Action | | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- | | API returns empty results | Broaden filters — check `armRegionName`, `serviceName`, `armSkuName` spelling | | User unsure of workload type | Ask clarifying questions; default to General Purpose D-series | | Region not specified | Use the canonical default from [copilot-instructions.md](../../copilot-instructions.md#azure-defaults-canonical); prices vary by region | | Unclear if VM or VMSS needed | Ask about scaling and instance count; default to single VM if unsure | | User asks VMSS pricing directly | Use same VM pricing API — VMSS has no extra charge; multiply by instance count | ## References - [Recommendation Workflow](references/recommendation-workflow.md) — Full 6-step procedure with decision tables and `web_fetch` URLs - [VM Family Guide](references/vm-families.md) — Family-to-workload mapping and selection - [Retail Prices API Guide](references/retail-prices-api.md) — Query patterns, filters, and examples - [VMSS Guide](references/vmss-guide.md) — When to use VMSS, orchestration modes, and autoscale patterns ## Reference Index Load these on demand — do NOT read all at once: | Reference | When to Load | | --------------------------------------- | ------------------------------------------------ | | `references/recommendation-workflow.md` | Full Steps 1–6 (decision tables, web_fetch URLs) | | `references/retail-prices-api.md` | Pricing queries (Step 4) | | `references/vm-families.md` | VM family selection (Step 3) | | `references/vmss-guide.md` | VMSS vs VM decision (Step 2) |
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
ROUTING SKILL — delegates to specialized diagram skills. USE FOR: any diagram request when the caller does not know which tool to use. Routes to drawio, python-diagrams, or mermaid based on diagram type.
Build and deploy GitHub Copilot SDK apps to Azure. WHEN: build copilot app, create copilot app, copilot SDK, @github/copilot-sdk, scaffold copilot project, copilot-powered app, deploy copilot app, host on azure, azure model, BYOM, bring your own model, use my own model, azure openai model, DefaultAzureCredential, self-hosted model, copilot SDK service, chat app with copilot, copilot-sdk-service template, azd init copilot, CopilotClient, createSession, sendAndWait, GitHub Models API.
Troubleshoot and resolve issues with Azure Messaging SDKs for Event Hubs and Service Bus. Covers connection failures, authentication errors, message processing issues, and SDK configuration problems. WHEN: event hub SDK error, service bus SDK issue, messaging connection failure, AMQP error, event processor host issue, message lock lost, send timeout, receiver disconnected, SDK troubleshooting, azure messaging SDK, event hub consumer, service bus queue issue, topic subscription error, enable logging event hub, service bus logging, eventhub python, servicebus java, eventhub javascript, servicebus dotnet, event hub checkpoint, event hub not receiving messages, service bus dead letter.
Authoritative reference for VS Code Copilot customization mechanisms: instructions, prompt files, custom agents, agent skills, MCP servers, hooks, and plugins. Use when deciding which customization type to use, creating new .instructions.md/.prompt.md/.agent.md/SKILL.md/mcp.json files from scratch, or debugging why a customization is not loading. DO NOT USE FOR: routine file edits where the format is already known.
Provides canonical entity counts from count-manifest.json. Use when agents need to reference how many agents, skills, instructions, or validators exist. Prevents hard-coded counts. WHEN: agent count, skill count, how many agents, how many skills, entity inventory, project statistics.