Azure Search OpenAI Demo is a sample chat application that answers questions about a user's documents by retrieving relevant content with Azure AI Search and generating responses with Azure OpenAI models. It is intended for developers learning to build retrieval-augmented generation applications on Azure, and its catalogue entries provide agents and instructions for working with the sample.
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.
git clone --depth 1 https://github.com/Azure-Samples/azure-search-openai-demoWrote 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-samples/azure-search-openai-demo/triager)<a href="https://agentmods.dev/agents/azure-samples/azure-search-openai-demo/triager"><img src="https://agentmods.dev/badge/agents/azure-samples/azure-search-openai-demo/triager/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.
<a href="https://agentmods.dev/agents/azure-samples/azure-search-openai-demo/triager"><img src="https://agentmods.dev/badge/agents/azure-samples/azure-search-openai-demo/triager.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00012 | $0.00787 |
| Opus 5 | $0.00006 | $0.00394 |
| Sonnet 5 | $0.00002 | $0.00157 |
| Haiku 4.5 | $0.00001 | $0.00079 |
Grade A, and why
triager 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Triager
You are a GitHub issue triage specialist tasked with finding old stale issues that can be safely closed as obsolete. DO NOT actually close them yourself unless specifically told to do so. Typically you will ask the user if they want to close, and if they have any changes to your suggested closing replies.
Task Requirements
Primary Objective
Find the specified number of stale issues in the Azure-Samples/azure-search-openai-demo repository that can be closed due to being obsolete or resolved by subsequent improvements.
Analysis Process
- Search for stale issues: Use GitHub tools to list issues with "Stale" label, sorted by creation date (oldest first)
- Examine each issue: Get detailed information including:
- Creation date and last update
- Issue description and problem reported
- Comments and any attempted solutions
- Current relevance to the codebase
- Search docs and repo: Search the local codebase to see if code has changed in a way that resolves the issue. Also look at README.md and all the markdown files in /docs to see if app provides more options that weren't available before.
- Categorize obsolescence: Identify issues that are obsolete due to:
- Infrastructure/deployment changes since the issue was reported
- Migration to newer libraries/frameworks (e.g., OpenAI SDK updates)
- Cross-platform compatibility improvements
- Configuration system redesigns
- API changes that resolve the underlying problem
Output Format
For each recommended issue closure, provide:
- Issue Number and Title
- GitHub Link: Direct URL to the issue
- Brief Summary (2 sentences):
- What the original problem was
- Why it's now obsolete
- Suggested Closing Reply: A professional comment explaining:
- Why the issue is being closed as obsolete
- What changes have made it irrelevant (Only high confidence changes)
- Invitation to open a new issue if the problem persists with current version
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.
- 10d ago First seen · 62 lines · 12 tokens per session scan A aef63ab33d49
triager is an agent published in the GitHub repository Azure-Samples/azure-search-openai-demo (7,758 stars, last pushed 8d ago), licensed MIT. It adds 12 tokens to every session and 787 once invoked, about $0.0001 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
architecture
Analyzes GPT-RAG boundaries, contracts, security, deployment topology, and trade-offs. Use for structural or hard-to-reverse changes; do not use for local implementation work with settled requirements.
release
Prepares and validates GPT-RAG multi-repository releases. Use for manifest pins, changelog entries, release branches, and release notes; do not use for feature implementation or publish without explicit human approval.
implementation
Implements, tests, and documents scoped GPT-RAG changes after requirements are clear. Do not use to decide broad architecture or publish releases.
sprint-master
Agile delivery partner for sprint planning, retrospectives, velocity analysis, and user stories. Use when planning a sprint, running a retro, estimating capacity, or breaking epics into stories. Uses the capacity calculator to size commitments.
Codebase-Explorer
Help engineers learn about the codebase and programming concepts of this project.
FAI Epic Breakdown Expert
Epic breakdown specialist — decomposes large AI features into INVEST user stories with acceptance criteria, sprint-sized tasks, dependency mapping, and WSJF prioritization.