GPT-RAG Data Ingestion is a service that processes documents such as PDFs, images, spreadsheets, transcripts, and SharePoint files so they can be searched through Azure AI Search. It prepares data with format-specific chunking and text or image embeddings for multimodal retrieval in agent-based applications.
Borrowing it
Nothing to install: this file belongs to Azure/gpt-rag-ingestion. 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/Azure/gpt-rag-ingestion/main/.github/skills/documentation-consistency/SKILL.mdgit clone --depth 1 https://github.com/Azure/gpt-rag-ingestionWrote 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-ingestion/documentation-consistency)<a href="https://agentmods.dev/skills/azure/gpt-rag-ingestion/documentation-consistency"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/documentation-consistency/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-ingestion/documentation-consistency"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/documentation-consistency.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 | $0.00031 | $0.00208 |
| Opus 5 | $0.00015 | $0.00104 |
| Sonnet 5 | $0.00006 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
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 10d 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
Ingestion documentation consistency
- Identify the user, operator, or cross-component behavior that changed.
- Search this repository for the format, configuration key, index field, endpoint, job type, contract, and previous terminology.
- Update ingestion-specific service or audit guidance in
README.md. - Search the
docsbranch ofAzure/GPT-RAGfor cross-component user and operator guidance and update every affected page in the coordinated change. - Register new published pages in that branch's
mkdocs.yml. - Ensure examples match current defaults, App Configuration labels, supported deployment modes, and released component behavior.
Keep the service README concise and link to https://azure.github.io/GPT-RAG/ for broad product guidance. Report the documentation branch or pull request in the implementation handoff.
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.
- 10d ago First seen · 21 lines · 31 tokens per session scan A 46cdfdf790e7
documentation-consistency is a skill published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 208 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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