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GPT-RAG: Instructions file for GitHub Copilot

.github/instructions/config-python.instructions.md

GPT-RAG config-python.instructions.md is an instructions file for GitHub Copilot from Azure/GPT-RAG. It costs 180 tokens per session, scanned A, original, MIT.

Project rules for Python modules that configure Azure services, including Azure OpenAI and search resources. They cover module boundaries, shared helpers, templates, errors, and tests that do not need live cloud credentials.

In plain words
What is it for?
Use them when adding Azure configuration, changing identity or security boundaries, updating resource templates, or writing mocked unit tests.
Why use it?
They help keep cloud configuration changes consistent, testable, and easier to diagnose.

Instructions file for GitHub Copilot ✓ vendor

Written for GitHub Copilot: a Copilot instructions file.

This is Azure/GPT-RAG's own configuration. It tells GitHub Copilot how to work on GPT-RAG 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 configures →

not rated 1.2krepo 1mo ago A scan 180 tokens original MIT
View source ↗ Azure/GPT-RAG aka.ms
About the project

GPT-RAG is an Azure solution accelerator for building agentic retrieval-augmented generation systems, where agents use connected organizational sources to ground their answers. It is for organizations deploying secure RAG applications with Microsoft Foundry, Microsoft Agent Framework, and Azure infrastructure. The catalogue add-ons provide agents, skills, and instructions for deploying and working with the accelerator.

Azure/GPT-RAG · 1,179 stars · on GitHub · aka.ms

Reuse

Borrowing it

Nothing to install: this file belongs to Azure/GPT-RAG. 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/main/.github/instructions/config-python.instructions.md
Clone the repo
git clone --depth 1 https://github.com/Azure/GPT-RAG

Made for: GitHub Copilot.

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.

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README.md
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Your own site
<a href="https://agentmods.dev/instructions/azure/gpt-rag/config-python"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag/config-python/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 GPT-RAG config-python.instructions.md

Your own site · 80×15
<a href="https://agentmods.dev/instructions/azure/gpt-rag/config-python"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag/config-python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 180 This file is loaded in full into every session.
When invoked 180 The same file — it is already loaded in full.
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 $0.00180 $0.00180
Opus 5.5 $0.00072 $0.00072
Sonnet 5.5 $0.00036 $0.00036
Haiku 4.5 $0.00018 $0.00018

Measured 2mo ago against content hash 5ac57c7161e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

GPT-RAG config-python.instructions.md 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/instructions/config-python.instructions.md · 21 lines

What it actually says

Python configuration modules

  • Keep modules focused on one Azure configuration responsibility.
  • Add new behavior under the appropriate config/<area>/ package rather than expanding an unrelated setup module.
  • Reuse existing credential, App Configuration, Key Vault, logging, rendering, and request helpers.
  • Use type hints and explicit boundary contracts.
  • Keep Search and Foundry IQ resources template-driven. Extend templates and inputs instead of hardcoding full resource payloads in Python.
  • Fail with actionable errors. Do not swallow exceptions, silently skip requested configuration, or use print for diagnostics.
  • Use mocked Azure boundaries in tests. Never require live credentials for a unit test.
  • Load engineering-principles for changes to Azure boundaries, identity, security, or shared configuration contracts.
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 · 21 lines · 180 tokens per session scan A 5ac57c7161e0

Subscribe to this mod's changes

GPT-RAG config-python.instructions.md is an instructions file published in the GitHub repository Azure/GPT-RAG (1,179 stars, last pushed 1mo ago), licensed MIT. It adds 180 tokens to every session, about $0.0007 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.

Data and AILanguagesPythonRAG and vectorsagent-frameworkagentic-ragazd-templatesazuregpt-4openai
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