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 julianobarbosa/claude-code-skills --skill azure-landing-zone-checklistgit clone --depth 1 https://github.com/julianobarbosa/claude-code-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/julianobarbosa/claude-code-skills/azure-landing-zone-checklist)<a href="https://agentmods.dev/skills/julianobarbosa/claude-code-skills/azure-landing-zone-checklist"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/azure-landing-zone-checklist/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/julianobarbosa/claude-code-skills/azure-landing-zone-checklist"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/azure-landing-zone-checklist.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 | $0.00146 | $0.03340 |
| Opus 5 | $0.00073 | $0.01670 |
| Sonnet 5 | $0.00029 | $0.00668 |
| Haiku 4.5 | $0.00015 | $0.00334 |
Grade A, and why
azure-landing-zone-checklist 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 12d 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Landing Zone Checklist Skill
This skill guides you through filling out the Microsoft Azure Landing Zone (ALZ) Accelerator checklist — the spreadsheet that captures all decisions needed before deploying an ALZ using Bicep or Terraform via the ALZ Accelerator tool.
The goal is to produce a filled Excel checklist where every decision is justified, best practices are applied by default, and items requiring human input are clearly flagged for the user's attention.
Why this matters
The ALZ Accelerator checklist is the single source of truth for a platform landing zone deployment. Getting it wrong means misconfigured networking, security gaps, or hours of rework. This skill ensures consistency by applying Microsoft's Cloud Adoption Framework (CAF) recommendations while respecting the user's existing infrastructure (IP ranges, subscriptions, naming conventions).
Workflow
Phase 1: Read the checklist
Read the uploaded .xlsx checklist using openpyxl to understand its structure. ALZ checklists typically have three tabs:
- Accelerator - Bootstrap: IaC type, VCS, subscriptions, naming, CI/CD settings
- Accelerator - Bicep: Scenario selection, component toggles, IP addressing, policies
- Accelerator - Terraform: Same as Bicep with additional options (AMBA, Sovereign LZ)
Parse the checklist to identify which fields already have values (column F = "Chosen Value") and which are empty. This tells you what the user has already decided vs. what needs input.
Phase 2: Interview the user
Gather decisions through structured questions. Ask in batches of 3-4 questions to avoid overwhelming the user. Prioritize in this order:
Batch 1 — Foundational decisions:
- IaC type (Bicep or Terraform)
- Version control system (Azure DevOps, GitHub, or local)
- Network topology scenario (Hub & Spoke vs vWAN, single vs multi-region, Azure Firewall vs NVA)
- Azure region
Batch 2 — Component decisions:
- Which components to deploy (DDoS, Private DNS, Bastion, VPN Gateway, ExpressRoute, Zero Trust)
- Security posture (AMA, Defender plans)
What ships with it
1 file 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.
- 12d ago First seen · 273 lines · 146 tokens per session scan A 4acc2f62b3d9
azure-landing-zone-checklist is a skill published in the GitHub repository julianobarbosa/claude-code-skills (10 stars, last pushed 16d ago), licensed MIT. It adds 146 tokens to every session and 3,340 once invoked, about $0.0007 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 2026-08-31.
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