Agent skill
openkbs-open-kbs-openkbs
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/design/openkbs-open-kbs-openkbs
SKILL.md
OpenKBS Development
OpenKBS is a framework for building AI-powered applications - from simple agents to full-stack platforms.
Two Usage Modes
1. Agent-Only Mode
Build a single conversational AI agent with backend handlers and custom UI.
openkbs create my-agent
openkbs push
Simplest way to get started - just an agent with memory, commands, and custom frontend.
2. Platform Mode (Full-Stack)
Build complete SaaS platforms that include agents plus additional infrastructure.
openkbs deploy # Deploy from openkbs.json
openkbs stack status # View deployed resources
Platform mode extends agent capabilities with:
- Multiple Agents: Run several agents on one platform (in
agents/folder) - Elastic Functions: Serverless Lambda (Node.js, Python, Java)
- Elastic Postgres: Managed PostgreSQL (Neon) for relational data
- Elastic Storage: S3 buckets + CloudFront CDN for files
- Elastic Pulse: Real-time WebSocket pub/sub
- Whitelabel: Custom domains (
example.com) with static site (site/folder)
Architecture Note: The whitelabel itself is a KB with its own kbId (a service agent, not user-facing). This "parent" kbId is used throughout the stack for elastic services. Each agent in agents/ has its own separate kbId. All agents share the platform's elastic services via the parent kbId.
Project Structure
Agent Structure
my-agent/
├── app/
│ ├── settings.json # Agent configuration (model, itemTypes, MCP)
│ └── instructions.txt # System prompt for LLM
├── src/
│ ├── Events/
│ │ ├── onRequest.js # Pre-process user messages
│ │ ├── onResponse.js # Parse LLM output, execute commands
│ │ ├── actions.js # Command implementations
│ │ ├── onCronjob.js # Scheduled periodic tasks
│ │ └── onPublicAPIRequest.js # Webhook handler
│ └── Frontend/
│ ├── contentRender.js # Custom React UI
│ └── contentRender.json # Frontend dependencies
└── openkbs.json # Elastic services config
Platform Structure
my-platform/
├── agents/ # Multiple AI agents
│ ├── marketing-assistant/ # Each agent has full structure
│ │ ├── app/
│ │ │ ├── settings.json
│ │ │ └── instructions.txt
│ │ └── src/
│ │ ├── Events/
│ │ │ ├── onRequest.js
│ │ │ ├── onResponse.js
│ │ │ └── actions.js
│ │ └── Frontend/
│ │ └── contentRender.js
│ └── support-agent/ # Another agent
│ ├── app/
│ └── src/
├── functions/ # Serverless Lambda functions
│ └── api/
│ └── index.mjs
├── site/ # Static site for whitelabel domain
│ └── index.html
└── openkbs.json # Elastic services config
openkbs.json
{
"elastic": {
"postgres": true,
"storage": true,
"pulse": true,
"functions": {
"api": { "runtime": "nodejs22.x", "memory": 512 }
}
}
}
Quick Commands
Agent Commands
openkbs create <name> # Create new agent
openkbs push # Deploy to cloud
openkbs pull # Download from cloud
openkbs update skills # Update this skill
Platform Commands
openkbs deploy # Deploy all elastic services
openkbs stack status # Show deployed resources
openkbs destroy # Remove all resources (DANGEROUS)
Elastic Services
openkbs fn list # List Lambda functions
openkbs fn push api # Deploy function
openkbs fn logs api # View function logs
openkbs postgres shell # Connect to Postgres
openkbs storage ls # List S3 objects
openkbs pulse status # WebSocket status
openkbs site push # Deploy static site
Backend Handler Pattern
Commands are XML tags with JSON content that the LLM outputs:
<commandName>{"param": "value"}</commandName>
The handler.js parses these tags, matches regex patterns in actions.js, and executes async functions:
// actions.js pattern
[/<googleSearch>([\s\S]*?)<\/googleSearch>/s, async (match) => {
const data = JSON.parse(match[1].trim());
const results = await openkbs.googleSearch(data.query);
return {
type: 'SEARCH_RESULTS',
data: results,
_meta_actions: ["REQUEST_CHAT_MODEL"] // Loop back to LLM
};
}]
Meta Actions
Control flow after command execution:
["REQUEST_CHAT_MODEL"]- Send result back to LLM for processing[]- Display result to user, stop conversation
Memory System
Configure in settings.json:
{
"itemTypes": {
"memory": {
"attributes": [
{ "attrName": "itemId", "attrType": "itemId", "encrypted": false },
{ "attrName": "body", "attrType": "body", "encrypted": true }
]
}
},
"options": {
"priorityItems": [{ "prefix": "memory_", "limit": 100 }]
}
}
Priority items are auto-injected into LLM context.
Additional Resources
- For backend SDK methods, see reference/backend-sdk.md
- For frontend React patterns, see reference/frontend-sdk.md
- For XML command definitions, see reference/commands.md
- For elastic services, see reference/elastic-services.md
- For complete examples, see examples/
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