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 agents/azure/gpt-rag/implementationgit 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 | $0.00030 | $0.00210 |
| Opus 5 | $0.00015 | $0.00105 |
| Sonnet 5 | $0.00006 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
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 yesterday.
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
GPT-RAG implementation
Follow AGENTS.md, .github/copilot-instructions.md, and all scoped
instructions that apply to the changed files.
Investigate the current implementation and tests, make the smallest coherent change, and preserve contracts and deployment behavior by default. Reuse existing modules, templates, scripts, and configuration paths.
Before editing, confirm acceptance criteria, affected repositories, security and compatibility risks, and documentation impact. Add or adjust behavioral tests, update affected documentation in the correct repository or branch, and run the existing validation specific to the change.
Input handoff: an issue, plan, or ADR with high-impact decisions resolved.
Output handoff: delivered behavior, changed files, commands and results, cross-repository dependencies, documentation status, and residual risks.
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.
- yesterday First seen · 25 lines · 30 tokens per session scan A a7a822101d0b
implementation is an agent published in the GitHub repository Azure/GPT-RAG (1,169 stars, last pushed 14d ago), licensed MIT. It adds 30 tokens to every session and 210 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.
Other agents, from other repositories
triager
Triage old stale issues for obsolescence and recommend closures.
fixer
Fix and verify issues in app.
Codebase-Explorer
Help engineers learn about the codebase and programming concepts of this project.
planner
Use this agent when the user needs a detailed implementation plan for a complex feature or task. Triggers on multi-step features, refactoring efforts, or tasks with unclear scope. Context: User starting a complex feature user: "I need to implement experiment comparison functionality" assistant: "I'll use the planner…
01-Orchestrator
Master orchestrator for the multi-step Azure platform engineering workflow. Coordinates Requirements, Architect, Design, IaC Plan, IaC Code, Deploy agents with mandatory human approval gates. Routes Bicep or Terraform tracks via decisions.iactool.
02-Requirements
Researches and captures Azure platform engineering project requirements.