GPT-RAG is an Azure solution accelerator for building agentic retrieval-augmented generation systems, where agents use connected organizational sources to ground their answers. It is for organizations deploying secure RAG applications with Microsoft Foundry, Microsoft Agent Framework, and Azure infrastructure. The catalogue add-ons provide agents, skills, and instructions for deploying and working with the accelerator.
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 Azure/GPT-RAG --skill engineering-principlesgit clone --depth 1 https://github.com/Azure/GPT-RAGWrote 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/azure/gpt-rag/engineering-principles)<a href="https://agentmods.dev/skills/azure/gpt-rag/engineering-principles"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag/engineering-principles/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/azure/gpt-rag/engineering-principles"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag/engineering-principles.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 | $0.00034 | $0.00179 |
| Opus 5.5 | $0.00014 | $0.00072 |
| Sonnet 5.5 | $0.00007 | $0.00036 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
engineering-principles 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.
What it actually says
GPT-RAG engineering principles
Load only the references needed for the task:
| When the task involves | Read |
|---|---|
| Repository purpose, boundaries, components, or Azure architecture | GPT-RAG architecture |
| Tests, validation, compatibility, or evidence | Testing and evidence |
| Identity, secrets, networking, retrieval security, or operations | Security and operations |
Use these principles as design questions rather than dogma. The task requirements, executable configuration, versioned contracts, and current implementation remain the sources of truth.
What ships with it
3 files 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.
- 1mo ago First seen · 19 lines · 34 tokens per session scan A c06a22399a4a
engineering-principles is a skill published in the GitHub repository Azure/GPT-RAG (1,179 stars, last pushed 20d ago), licensed MIT. It adds 34 tokens to every session and 179 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 2026-08-30.
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