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/agents/release.agent.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/agents/azure/gpt-rag-ingestion/release)<a href="https://agentmods.dev/agents/azure/gpt-rag-ingestion/release"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/release.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 | $0.00042 | $0.00212 |
| Opus 5 | $0.00021 | $0.00106 |
| Sonnet 5 | $0.00008 | $0.00042 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
release 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 7d 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 release
Follow AGENTS.md, the complete rules in
.github/copilot-instructions.md, and load service-release.
Prepare release branches from develop, keep the branch, VERSION,
changelog, tag, and GitHub Release title synchronized, and validate the exact
ingestion artifact against its compatible Azure/GPT-RAG release context.
Release branches contain no unrelated product work.
Public validation notes must not expose personal Azure environment or resource-group names. Never create or edit a tag, GitHub Release, image, package, or production deployment without explicit human approval.
Output handoff: proposed version, compatibility context, release artifacts, commands and results, documentation status, rollback path, and remaining approval actions.
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.
- 7d ago First seen · 24 lines · 42 tokens per session scan A d0f2d97deedb
release is an agent published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed 3d ago), licensed MIT. It adds 42 tokens to every session and 212 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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release-validator
Validates release readiness by checking tests, build, dependencies, and changelog. Use before creating a release.
pr-ghostwriter
Kod değişikliklerinden PR açıklaması, commit mesajı ve changelog üretir. Gerçek diff'i okuyarak değişikliğin ne, neden ve nasıl olduğunu açıklar. Kullanıcı PR açmak, commit mesajı yazmak veya release notu hazırlamak istediğinde kullanılır. Jenerik açıklama üretmez — her zaman gerçek değişikliğe özgü yazar.
iris
GitHub operations specialist — branches, pull requests, issues, releases, tags. Called by zeus after review. Never pushes or merges without explicit human approval. Integrates with VS Code GitHub Pull Requests extension.
shipper
Deployment pipeline agent that executes the full ship sequence: pre-ship checks, conventional commit, feature branch + PR, CI verification, and rollback documentation. Use in Phase 5 after Gate 2 passes. Never commits directly to main. Not for implementation or review approval.