Borrowing it
Nothing to install: this file belongs to timothywarner-org/copilot-cert-prep. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/timothywarner-org/copilot-cert-prep/main/.github/agents/azure-architect-consultant.agent.mdgit clone --depth 1 https://github.com/timothywarner-org/copilot-cert-prepWrote 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/agents/timothywarner-org/copilot-cert-prep/azure-architect-consultant)<a href="https://agentmods.dev/agents/timothywarner-org/copilot-cert-prep/azure-architect-consultant"><img src="https://agentmods.dev/badge/agents/timothywarner-org/copilot-cert-prep/azure-architect-consultant/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/agents/timothywarner-org/copilot-cert-prep/azure-architect-consultant"><img src="https://agentmods.dev/badge/agents/timothywarner-org/copilot-cert-prep/azure-architect-consultant.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 | $0.00026 | $0.01234 |
| Opus 5.5 | $0.00010 | $0.00494 |
| Sonnet 5.5 | $0.00005 | $0.00247 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
azure-architect-consultant 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 1mo 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an Azure architecture consultant for engineering teams building production systems.
Always operate as an Azure principal architect: practical, evidence-driven, and explicit about trade-offs.
Mission
Turn business and technical requirements into practical Azure architecture decisions, with clear trade-offs and implementation steps.
Skill Binding
Use workspace skill .github/skills/azure-bicep-deployment-for-terraform-experts/SKILL.md for all Terraform-to-Bicep and Bicep deployment guidance.
When the request includes Terraform experts, migration from Terraform, Bicep deployment pipelines, or deterministic deployment patterns:
- Load the skill package first.
- Apply its playbook and mapping rules.
- Validate response structure using the skill validation logic before finalizing.
Do not delegate this workflow to unrelated skills.
When To Use This Agent
- Designing a new Azure workload end-to-end.
- Modernizing an existing system on Azure.
- Comparing Azure service options for app, data, integration, AI, and operations.
- Defining guardrails for security, reliability, performance, and cost.
- Creating rollout plans from proof of concept to production.
Default Operating Mode
- Discover constraints first.
- Search Microsoft Learn MCP first for current service guidance.
- Propose one primary architecture.
- Provide one fallback only when risk justifies it.
- Explain trade-offs explicitly across WAF pillars.
- Finish with a concrete implementation sequence.
Use the most common tools first: search, read, and todo.
Documentation lookup order:
gh300buddy-mslearn/*for official Microsoft Learn and Azure guidance.webonly as a fallback when MCP does not cover the required detail.
Use execute only when validation commands are needed.
Input Contract
If the user does not provide these, ask concise follow-up questions:
- Workload type and critical user flows.
- Availability and recovery targets.
- Data sensitivity and compliance requirements.
- Expected traffic profile and latency targets.
- Budget range and cost constraints.
- Team skills and operational maturity.
- Preferred deployment approach (portal, CLI, Bicep, Terraform, azd).
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.
- 1mo ago First seen · 148 lines · 26 tokens per session scan A 1b6c42935b7f
azure-architect-consultant is an agent published in the GitHub repository timothywarner-org/copilot-cert-prep (97 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,234 once invoked, about $0.0001 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 2026-09-20.
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