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/ingestion-validation/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/ingestion-validation)<a href="https://agentmods.dev/skills/azure/gpt-rag-ingestion/ingestion-validation"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/ingestion-validation/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/ingestion-validation"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/ingestion-validation.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 | $0.00042 | $0.00343 |
| Opus 5 | $0.00021 | $0.00171 |
| Sonnet 5 | $0.00008 | $0.00069 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
ingestion-validation 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 11d 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 validation
Start with the narrowest existing command that covers the change.
Engineering-agent assets
python -m pip install --requirement .github/scripts/requirements.txt
python .github/scripts/validate-agentic-assets.py
Python
Run focused tests first:
python -m pytest tests/<affected_test>.py
Then run the maintained suite when risk or repository policy requires it:
python -m pytest
Do not require live Azure credentials for unit tests. Reuse existing fakes, stubs, and golden fixtures.
Frontend
From frontend/, use the scripts defined in package.json:
npm test
npm run lint
npm run build
Runtime integration
For changes to formats, sources, jobs, embedding, Search payloads, or deployment:
- Build and run the existing container or use the applicable
scripts/deploy.ps1orscripts/deploy.shpath. - Exercise a representative source and document.
- Verify the terminal job result and expected Azure AI Search documents, fields, ACL metadata, and deletion behavior.
- Inspect logs and correlated audit events without copying document content, credentials, or private environment names into public artifacts.
If a command is unavailable or live validation is unsafe, report the missing dependency and residual risk. Do not substitute an unrelated passing check.
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.
- 11d ago First seen · 59 lines · 42 tokens per session scan A 4de178738cf3
ingestion-validation is a skill published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 343 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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