ERF Knowledge Assistant

Featured Assistant

ERF Specialized Assistant

Use this assistant to answer questions about the ERF code base, interpret documentation, and keep responses grounded in ERF-specific sources.

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Open the ERF Specialized Assistant
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Context7 ERF Chatbot

Use the Context7 ERF chatbot for an MCP-backed ERF chat surface, or open the ERF and AMReX chatbots directly in Context7.

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Assistant

Documented Here

MCP

ERF-specific

ERF Specialized Assistant

Yes

No

Yes
Sphinx SingleHTML
Deep research (AMReX/ERF)

No

No

Yes
Sphinx SingleHTML
Source/Exec

No

No

Yes

Context7 ERF chatbot

Yes

Yes

Yes

Context7 AMReX chatbot

Yes

Yes

No

ERF users can use a specialized assistant to answer questions about the code base, help interpret documentation, and keep responses grounded in ERF-specific sources. Use the button above to open the ready-to-use assistant.

This page collects the small, reusable assets that matter for ERF assistant setup. It is intentionally separate from ERF Agentic Workflow, which covers running AMReX-Agent with ERF.

How the assistant adapts

The ERF assistant can adapt its level of detail to the user’s background. At the start of a session, it can ask whether you are new to HPC / AMReX / ERF, familiar with HPC but newer to ERF, or an experienced ERF / AMReX user. That answer shapes how much context, terminology, and step-by-step detail it provides.

It can also help author or modify ERF inputs files. In that mode, it should ask a few clarifying questions first, then produce a commented template using only documented parameter keys. Any unconfirmed key should be marked explicitly and left for verification.

No advanced tools are enabled by default. If available in your assistant environment, Guided Learning can convert the exchange into a structured, step-by-step instructional workflow, while Deep Research can be used to assemble a more evidence-heavy answer by gathering and synthesizing larger amounts of documentation.

User expertise and assistant mode

The assistant distinguishes between two separate forms of adaptation.

  1. User expertise leveling controls the style of explanation. A novice user receives more definitions, more context, and more explicit step-by-step guidance. An experienced user receives a terser response with less framing. This concerns presentation, not the underlying evidence standard.

  2. Tool selection controls how the assistant gathers or structures material. Guided Learning is suited to a structured instructional sequence, while Deep Research is suited to a broader evidence-gathering pass before the response is written. These tools are optional and not enabled by default.

The goal in both cases is to keep the assistant evidence-first: it should avoid guessing ERF parameters, inventing undocumented keys, or claiming certainty without supporting inputs.

What to use

Asset

Purpose

Download

AMReX Framework Core Prompt

Layer 1 prompt for AMReX framework concepts and evidence boundaries.

Prompt

AMReX Framework Core Report

Layer 1 compiled report used by the assistant.

Report

Portable HPC / Numerics Prompt

Layer 2 prompt for reproducibility, numerics, and debugging policy.

Prompt

Portable HPC / Numerics Report

Layer 2 compiled report used by the assistant.

Report

Layer 3 Prompt A

Generic Layer 3 prompt optimized for ChatGPT.

Prompt

Layer 3 Prompt B

Generic Layer 3 prompt optimized for Gemini.

Prompt

Layer 3 Prompt C

Generic Layer 3 prompt variant from the tutorial set.

Prompt

Layer 3 Prompt D

Generic Layer 3 prompt variant from the tutorial set.

Prompt

ERF Layer 3 Generation Prompt

ERF-specific Layer 3 generation prompt.

Prompt

ERF Layer 3 Context Report

ERF-specific Layer 3 context report.

Report

ERF Layer 3 Instruction Prompt

ERF-specific Layer 3 instruction prompt.

Prompt

Suggested setup

  1. Open the ERF Specialized Assistant if you want a ready-made starting point.

  2. If you want to build your own assistant, use the ERF Layer 3 context report and instruction prompt.

  3. If you need to extend the assistant for a new ERF-specific workflow, update the prompt and context files together.

  4. If you are writing a new simulation setup, use the assistant in inputs-authoring mode and share the scenario, domain, and hardware details first.

This keeps the assistant anchored to the same ERF sources used to generate the existing profile.