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/architecture-decision/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/architecture-decision)<a href="https://agentmods.dev/skills/azure/gpt-rag-ingestion/architecture-decision"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/architecture-decision/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/architecture-decision"><img src="https://agentmods.dev/badge/skills/azure/gpt-rag-ingestion/architecture-decision.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 | $0.00043 | $0.00222 |
| Opus 5.5 | $0.00017 | $0.00089 |
| Sonnet 5.5 | $0.00009 | $0.00044 |
| Haiku 4.5 | $0.00004 | $0.00022 |
Grade A, and why
architecture-decision 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 2mo 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 architectural decision
- Load the relevant
engineering-principlesreferences. - Define the retrieval or operator outcome, constraints, affected repositories, and up to five prioritized characteristics with measures.
- Compare at least two viable alternatives and not changing.
- Evaluate document security, data contracts, index compatibility, chunk quality, throughput, memory, cost, failure recovery, migration, and reversibility.
- Record the decision under
docs/adr/using the ADR template. - Define fitness functions, adoption order, rollback or reindex strategy, and a review trigger.
Do not turn a tool or Azure service preference into an architectural requirement. When evidence is missing, record a time-bounded investigation and its decision criterion rather than guessing.
What ships with it
1 file 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.
- 2mo ago First seen · 23 lines · 43 tokens per session scan A 137be5acf476
architecture-decision is a skill published in the GitHub repository Azure/gpt-rag-ingestion (188 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 222 once invoked, about $0.0002 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.
architecture-decision compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| architecture-decisionAzure/gpt-rag-ingestion | Skill | 188 | 1mo ago | MIT | A | 43 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |