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 autohandai/community-skills --skill azure-computegit clone --depth 1 https://github.com/autohandai/community-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/autohandai/community-skills/azure-compute)<a href="https://agentmods.dev/skills/autohandai/community-skills/azure-compute"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/azure-compute/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/autohandai/community-skills/azure-compute"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/azure-compute.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 | $0.00145 | $0.02155 |
| Opus 5 | $0.00072 | $0.01077 |
| Sonnet 5 | $0.00029 | $0.00431 |
| Haiku 4.5 | $0.00015 | $0.00215 |
Grade A, and why
azure-compute 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 8d 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.
This is a copy
100% identical to azure-compute — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
Workflow
Use reference files for initial filtering
CRITICAL: then always verify with live documentation from learn.microsoft.com before making final recommendations. If
web_fetchfails, use reference files as fallback but warn the user the information may be stale.
Step 1: Gather Requirements
Ask the user for (infer when possible):
| Requirement | Examples |
|---|---|
| Workload type | Web server, relational DB, ML training, batch processing, dev/test |
| vCPU / RAM needs | "4 cores, 16 GB RAM" or "lightweight" / "heavy" |
| GPU needed? | Yes → GPU families; No → general/compute/memory |
| Storage needs | High IOPS, large temp disk, premium SSD |
| Budget priority | Cost-sensitive, performance-first, balanced |
| OS | Linux or Windows (affects pricing) |
| Region | Affects availability and price |
| Instance count | Single instance, fixed count, or variable/dynamic |
| Scaling needs | None, manual scaling, autoscale based on metrics or schedule |
| Availability needs | Best-effort, fault-domain isolation, cross-zone HA |
| Load balancing | Not needed, Azure Load Balancer (L4), Application Gateway (L7) |
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.
- 8d ago First seen · 161 lines · 145 tokens per session scan A be1416a7f9f4
azure-compute is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 145 tokens to every session and 2,155 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to azure-compute, differing in 0 lines, and is treated as a copy.
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