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gpt-rag-ingestion: Agent for Claude Code

.github/agents/implementation.agent.md

implementation is an agent for Claude Code from Azure/gpt-rag-ingestion. It costs 38 tokens per session (237 once invoked), scanned A, original, MIT.

A scoped implementation role for making, testing, and documenting changes to a document-ingestion system that processes files for search or retrieval. It is intended for work whose requirements and major design decisions are already settled.

In plain words
What is it for?
Use it to add document formats, update ingestion code, modify thin API handlers, maintain asynchronous processing, run focused tests, and report changed files, compatibility checks, deployment evidence, and remaining risks.
Why use it?
It keeps changes focused and preserves important behavior such as document chunking, search indexes, permissions, configuration, job tracking, and audit records.

Agent for Claude Code ✓ vendor

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: mentions AGENTS.md.

This is Azure/gpt-rag-ingestion's own configuration. It tells Claude Code how to work on gpt-rag-ingestion itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gpt-rag-ingestion configures →

not rated 188repo 1mo ago A scan 38 tokens original MIT
View source ↗ Azure/gpt-rag-ingestion
About the project

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.

Azure/gpt-rag-ingestion · 188 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Azure/gpt-rag-ingestion/main/.github/agents/implementation.agent.md
Clone the repo
git clone --depth 1 https://github.com/Azure/gpt-rag-ingestion

Made for: Claude Code.

Wrote 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.

agentmods badge for implementation

README.md
[![agentmods](https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/implementation/github.svg)](https://agentmods.dev/agents/azure/gpt-rag-ingestion/implementation)
Your own site
<a href="https://agentmods.dev/agents/azure/gpt-rag-ingestion/implementation"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/implementation/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.

agentmods 80×15 button for implementation

Your own site · 80×15
<a href="https://agentmods.dev/agents/azure/gpt-rag-ingestion/implementation"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/implementation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 237 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.00237
Opus 5.5 $0.00015 $0.00095
Sonnet 5.5 $0.00008 $0.00047
Haiku 4.5 $0.00004 $0.00024

Measured 2mo ago against content hash 3d4a63f720b1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

implementation 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.

.github/agents/implementation.agent.md · 27 lines

What it actually says

Ingestion implementation

Follow AGENTS.md, .github/copilot-instructions.md, and all scoped instructions that apply to changed files. Load engineering-principles and ingestion-validation when relevant.

Investigate current implementation and tests, then make the smallest coherent change. Preserve chunk/index contracts, source metadata, authorization, configuration precedence, job lifecycle, and audit semantics by default.

Add document formats through chunking/chunker_factory.py and a focused chunker. Keep API handlers thin, async paths non-blocking, Azure boundaries explicit, and failures visible through configured logging.

Input handoff: an issue, plan, incident reproduction, or ADR with high-impact decisions resolved.

Output handoff: delivered behavior, changed files, commands and results, Search/config/contract compatibility, documentation status, deployment evidence, and residual risks.

Changes

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.

  1. 2mo ago First seen · 27 lines · 38 tokens per session scan A 3d4a63f720b1

Subscribe to this mod's changes

implementation is an agent published in the GitHub repository Azure/gpt-rag-ingestion (188 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 237 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.

Backend and APIsDocuments
Related

implementation compared with agents from other repositories

implementation next to 6 agents from other repositories
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implementationAzure/gpt-rag-ingestionAgent1881mo agoMITA38Claude Code
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OpenAPI to Application Generatorarchubbuck/workspace-architectAgent192d agoISCA16Claude Code
Feishu Integration Developerandywxy1/ceo-pluginAgent67mo agoGPL-3.0B69Claude Code
php-developerdavepoon/buildwithclaudeAgent3.7kyesterdayMITA51Claude Code
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