name: backend-ai-guide description: | Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read

Backend.AI Guide Skill

Purpose

This skill provides expert-level information about the Backend.AI platform by:

• Fetching official documentation from the Backend.AI GitHub repository
• Recursively exploring Major Components documentation links
• Following relevant links to gather comprehensive technical details
• Providing accurate, source-backed answers about Backend.AI architecture and features

When to Use

Activate this skill when the user asks about:

• Backend.AI platform overview or architecture
• Backend.AI components (Manager, Agent, Storage Proxy, Webserver, App Proxy)
• Backend.AI setup, requirements, or infrastructure
• Backend.AI APIs (REST, GraphQL)
• Backend.AI features (session scheduling, resource allocation, multi-tenancy)
• Backend.AI kernels, containers, or runtime elements
• How the WebUI connects to or interacts with Backend.AI backend
• Differences between Backend.AI components

Primary Documentation Sources

1. Main README: https://github.com/lablup/backend.ai/blob/main/README.md

   • Overview and architecture
   • Major Components section with component links
   • Requirements and setup information

2. Major Component READMEs: Follow links from the Major Components section

   • Manager component details
   • Agent component details
   • Storage Proxy details
   • Webserver details
   • App Proxy details
   • And other components

3. Recursive Link Following: When a component README references additional documentation, follow those links to gather comprehensive information

Instructions

Step 1: Identify the Question Scope

• Determine what aspect of Backend.AI the user is asking about
• Identify which components or features are relevant

Step 2: Fetch the Main README

• Always start by fetching: https://github.com/lablup/backend.ai/blob/main/README.md
• Extract key information relevant to the question
• Identify links to Major Components that need to be explored

Step 3: Recursively Fetch Component Documentation

• For questions about specific components, fetch their individual READMEs
• Component README links are found in the "Major Components" section
• Example component paths (adjust based on actual links):
  • Manager: src/ai/backend/manager/README.md
  • Agent: src/ai/backend/agent/README.md
  • Storage Proxy: src/ai/backend/storage/README.md
  • Webserver: src/ai/backend/web/README.md
  • App Proxy: src/ai/backend/appproxy/README.md

Step 4: Follow Additional Links

• If component READMEs reference additional documentation, follow those links
• Common additional documentation types:
  • Architecture diagrams
  • API documentation
  • Configuration guides
  • Development guides
• Important: Only follow links that are relevant to answering the user's question

Step 5: Synthesize and Present Information

• Combine information from all fetched sources
• Structure the answer logically:
  1. Direct answer to the user's question
  2. Supporting details from official documentation
  3. Related component interactions (if applicable)
  4. Links to source documentation for further reading
• Use clear headings and formatting
• Include code examples or configuration snippets when relevant

Best Practices

1. Always Cite Sources

   • Reference the specific documentation URLs you fetched
   • Help users find more detailed information

2. Stay Current

   • Fetch documentation fresh each time (don't rely on cached knowledge)
   • Note version requirements (Python, Docker, PostgreSQL, etc.)

3. Explain Component Interactions

   • Backend.AI is a distributed system - explain how components work together
   • Clarify the relationship between WebUI (this project) and Backend.AI backend

4. Be Precise with Technical Details

   • Include version numbers, requirements, and configuration details
   • Distinguish between different API types (REST vs GraphQL)

5. Limit Recursion Depth

   • Fetch main README + relevant component READMEs
   • Only follow 1-2 additional link levels unless user needs deep details
   • Balance thoroughness with response time

Example Question Types

Architecture Questions

• "How does Backend.AI work?"
• "What is the architecture of Backend.AI?"
• "What are the main components of Backend.AI?"

Component Questions

• "What does the Backend.AI Manager do?"
• "How does the Agent component work?"
• "What is the Storage Proxy?"

Integration Questions

• "How does this WebUI connect to Backend.AI?"
• "What APIs does Backend.AI expose?"
• "How do I authenticate with Backend.AI?"

Setup Questions

• "What are the requirements for Backend.AI?"
• "How do I set up Backend.AI?"
• "What infrastructure does Backend.AI need?"

Response Format

Structure answers as follows:

## [Direct Answer to Question]

[Concise, direct answer based on official documentation]

## Details

[Supporting information from fetched documentation]

### Component Interactions (if applicable)

[How different components work together]

## Technical Specifications (if applicable)

- Requirements: [versions, dependencies]
- Configuration: [relevant settings]
- APIs: [REST/GraphQL endpoints]

## Source Documentation

- Main: [URL to main README]
- Component: [URLs to component READMEs]
- Additional: [URLs to other relevant docs]

Notes

• Backend.AI is the backend platform that this WebUI project connects to
• This WebUI (backend.ai-webui) is a client application that uses Backend.AI's APIs
• When users ask about "the backend" in this project context, they likely mean Backend.AI
• Distinguish between WebUI code (this project) and Backend.AI platform code (separate repo)

Limitations

• This skill only fetches publicly available GitHub documentation
• For questions requiring internal documentation or specific deployment details, direct users to Backend.AI team
• Cannot access private repositories or non-public documentation