Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/azure/gpt-rag/lifecycle-hooksgit clone --depth 1 https://github.com/Azure/GPT-RAGWhat 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 | $0.00159 | $0.00159 |
| Opus 5 | $0.00079 | $0.00079 |
| Sonnet 5 | $0.00032 | $0.00032 |
| Haiku 4.5 | $0.00016 | $0.00016 |
Grade A, and why
GPT-RAG lifecycle-hooks.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 3d 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
azd lifecycle hooks
- Keep PowerShell and shell hooks behaviorally equivalent.
- Treat hook ordering and environment-variable propagation as public deployment behavior.
- Preserve
azdenvironment reuse when component repositories are cloned and deployed. - Do not edit generated content under
infra/; update root overrides or the pinned infrastructure source. - Quote paths and external input safely. Do not echo secrets or private Azure validation environment names.
- Surface failed prerequisites and provisioning steps; do not continue with a success-shaped fallback.
- Validate both platform variants when changing shared hook behavior.
- Load
documentation-consistencywhen the deployment flow or operator steps change.
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.
- 3d ago First seen · 21 lines · 159 tokens per session scan A 84753a3866a1
GPT-RAG lifecycle-hooks.instructions.md is an instructions file published in the GitHub repository Azure/GPT-RAG (1,169 stars, last pushed 15d ago), licensed MIT. It adds 159 tokens to every session, about $0.0008 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.
Other instructions, from other repositories
azure-search-openai-demo AGENTS.md
Instructions for Azure-Samples/azure-search-openai-demo, covering instructions for coding agents, overall code layout, adding new data, adding a new azd environment variable and adding a new setting to "developer settings" in rag app.
azure-search-openai-demo bicep.instructions.md
Infrastructure as Code with Bicep.
chat-with-your-data-solution-accelerator copilot-instructions.md
Instructions for Azure-Samples/chat-with-your-data-solution-accelerator, covering chat with your data (cwyd) — repository instructions, repository layout (truth), mandatory references — consult before editing, external pattern sources — read-only and hard rules.
openai-cookbook AGENTS.md
Instructions for openai/openai-cookbook, covering repository guidelines, project structure & module organization, build, test, and development commands, coding style & naming conventions and testing guidelines.
AI-Gateway AGENTS.md
Instructions for Azure-Samples/AI-Gateway, covering agents.md, directory structure, labs/, modules/ and shared/.
OpenAIWorkshop copilot-instructions.md
Instructions for microsoft/OpenAIWorkshop, covering workflow orchestration, 1. plan node default, 2. subagent strategy, 3. self-improvement loop and 4. verification before done.