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 agentmods add skills/azure/gpt-rag/documentation-consistencynpx skills add Azure/GPT-RAG --skill documentation-consistencygit 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/documentation-consistency)<a href="https://agentmods.dev/skills/azure/gpt-rag/documentation-consistency"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag/documentation-consistency.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00223 |
| Opus 5 | $0.00019 | $0.00112 |
| Sonnet 5 | $0.00008 | $0.00045 |
| Haiku 4.5 | $0.00004 | $0.00022 |
Grade A, and why
documentation-consistency 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 5d 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 documentation consistency
User-facing documentation lives on the docs branch of Azure/GPT-RAG and is
published at https://azure.github.io/GPT-RAG/.
- Identify the user or operator behavior that changed.
- Search the documentation source for the feature, configuration key, parameter, component, and previous terminology.
- Update every affected page in the same coordinated change.
- Register new pages in
mkdocs.yml. - Keep repository and service READMEs concise; link to the published site instead of duplicating product guidance.
- Ensure examples match current defaults, supported deployment modes, and shipped component versions.
- Report the documentation branch or pull request in the implementation handoff.
A user-visible change is incomplete until documentation is updated or the search demonstrates that no published page is affected.
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.
- 5d ago First seen · 25 lines · 39 tokens per session scan A ac0e10af1f65
documentation-consistency is a skill published in the GitHub repository Azure/GPT-RAG (1,170 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 223 once invoked, about $0.0002 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 2026-08-30.
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