Skip to main content 홈 크리에이터 stacksjs reportshq stacks-ai
stacks-ai Use when integrating AI capabilities into a Stacks application — using Anthropic/OpenAI/Ollama/AWS Bedrock drivers, image generation (DALL-E), vision analysis, RAG/vector search, embeddings, MCP (Model Context Protocol) clients, text summarization, sentiment analysis, content classification, personalization, or the buddy AI assistant. Covers @stacksjs/ai and config/ai.ts.
설치로 이동 Skills Marketplace 커뮤니티가 만든 AI 스킬을 발견하고 탐색하세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/stacksjs/reportshq --skill stacks-ai명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Zip 다운로드 다운로드 중...
Use when implementing authentication, authorization, passkeys, TOTP/2FA, RBAC, gates, policies, session auth, token management, email verification, password resets, or rate limiting in a Stacks application. Covers the @stacksjs/auth package, config/auth.ts, app/Gates.ts, and app/Middleware/.
Use when building e-commerce features in Stacks — the commerce namespace with 13 sub-modules (products, orders, customers, coupons, payments, shipping, tax, gift cards, waitlists, devices, receipts, restaurant), 20+ commerce models, default commerce functions, or the commerce configuration. Covers @stacksjs/commerce.
name stacks-ai description Use when integrating AI capabilities into a Stacks application — using Anthropic/OpenAI/Ollama/AWS Bedrock drivers, image generation (DALL-E), vision analysis, RAG/vector search, embeddings, MCP (Model Context Protocol) clients, text summarization, sentiment analysis, content classification, personalization, or the buddy AI assistant. Covers @stacksjs/ai and config/ai.ts. license MIT compatibility Bun >= 1.3.0, TypeScript allowed-tools Read Edit Write Bash Grep Glob
Stacks AI
Comprehensive AI/LLM integration with 4 provider drivers, image generation, vision, RAG, MCP support, and personalization.
Key Paths
Core package: storage/framework/core/ai/src/
Configuration: config/ai.ts
Source Files
ai/src/
├── drivers/
│ ├── anthropic.ts # Claude driver
│ ├── openai.ts # GPT + DALL-E + Whisper + TTS
│ ├── ollama.ts # Local LLM driver
│ └── bedrock.ts # AWS Bedrock utilities
├── image.ts # Image generation & vision
├── search.ts # RAG, embeddings, vector index
├── mcp.ts # Model Context Protocol client
├── personalization.ts # Sentiment, classification, recommendations
├── buddy.ts # AI coding assistant
├── claude-agent.ts # Claude CLI agent (local & EC2)
├── claude-agent-sdk.ts # Claude Agent SDK driver
└── text.ts # Bedrock text summarization
Anthropic Driver import { anthropic } from '@stacksjs/ai'
anthropic.configure ({ apiKey : '...' , model : 'claude-sonnet-4-20250514' , maxTokens : 4096 })
const result = await anthropic.chat ([{ role : 'user' , content : 'Hello' }])
const stream = await anthropic.streamChat (messages, options)
const response = await anthropic.prompt ('Summarize this text...' )
const tokens = anthropic.estimateTokens (text)
OpenAI Driver import { openai } from '@stacksjs/ai'
openai.configure ({ apiKey : '...' , model : 'gpt-4o' , embeddingModel : 'text-embedding-3-small' })
const result = await openai.chat (messages, { temperature : 0.7 })
const stream = await openai.streamChat (messages)
const embeddings = await openai.embed ('text to embed' )
const image = await openai.generateImage ('a sunset over mountains' )
const transcription = await openai.transcribe (audioFile)
const speech = await openai.textToSpeech ('Hello world' )
Provider-Neutral Client Use the config-driven client for application features that can run against
Anthropic, OpenAI, or Ollama. Configuration inspection is safe to return from a
status endpoint because it never includes credentials.
import { createAIClient, getAIProviderConfiguration } from '@stacksjs/ai'
import aiConfig from './config/ai'
const configuration = getAIProviderConfiguration (aiConfig)
if (configuration.configured ) {
const client = createAIClient (aiConfig)
const result = await client.generate ([{ role : 'user' , content : 'Draft a launch plan.' }])
}
Ollama Driver (Local LLMs) import { ollama } from '@stacksjs/ai'
ollama.configure ({ host : 'http://localhost:11434' , model : 'llama3' })
const result = await ollama.chat (messages)
const stream = await ollama.streamChat (messages)
const text = await ollama.generate ('Write a poem' )
const embeddings = await ollama.embed ('text' )
const models = await ollama.listModels ()
await ollama.pullModel ('llama3' , (progress ) => console .log (progress))
await ollama.deleteModel ('old-model' )
const info = await ollama.showModel ('llama3' )
const running = await ollama.isRunning ()
Image Generation import { generateImage, editImage, createImageVariation, analyzeImage, analyzeImages } from '@stacksjs/ai'
const result = await generateImage ('a cat in space' , {
model : 'dall-e-3' , size : '1024x1024' , quality : 'hd' , style : 'vivid' , n : 1
})
await editImage (imageInput, 'add a hat' , { mask : maskInput })
await createImageVariation (imageInput, { n : 3 })
const analysis = await analyzeImage ({ url : 'https://...' }, 'What is in this image?' )
const multiAnalysis = await analyzeImages ([img1, img2], 'Compare these' )
Image inputs: { url: string }, { base64: string }, { file: string } (auto-converted)
RAG & Vector Search import { createEmbedding, rag, VectorIndex , chunkText, indexText } from '@stacksjs/ai'
const embedding = await createEmbedding ('text to embed' )
cosineSimilarity (vecA, vecB)
dotProduct (vecA, vecB)
euclideanDistance (vecA, vecB)
const chunks = chunkText (longText, { chunkSize : 500 , overlap : 50 })
const index = await indexText (text, { chunkSize : 500 })
const answer = await rag ('What is X?' , index, { model : 'claude-sonnet-4-20250514' , maxTokens : 1000 })
const idx = new VectorIndex ({ dimensions : 1536 })
await idx.add ([{ id : '1' , content : 'Hello' , metadata : {} }])
const results = await idx.search ('greeting' , 5 )
const results2 = await idx.searchByVector (queryEmbedding, 5 )
idx.remove ('1' )
idx.clear ()
idx.size
idx.ids
MCP (Model Context Protocol) import { MCPClient , MCPManager , connectStdio, connectHTTP } from '@stacksjs/ai'
const client = new MCPClient ({ name : 'my-server' , transport : 'stdio' , command : 'npx' , args : ['my-mcp-server' ] })
await client.connect ()
const tools = await client.listTools ()
const resources = await client.listResources ()
const prompts = await client.listPrompts ()
const result = await client.callTool ('tool-name' , { arg : 'value' })
const resource = await client.readResource ('resource://path' )
const prompt = await client.getPrompt ('prompt-name' , { arg : 'value' })
const anthropicTools = client.toAnthropicTools ()
const openaiTools = client.toOpenAITools ()
const manager = new MCPManager ()
manager.addServer ({ name : 'server1' , transport : 'stdio' , command : '...' })
manager.addServer ({ name : 'server2' , transport : 'streamable-http' , url : '...' })
const allTools = await manager.getAllTools ()
await manager.callTool ('server1/tool-name' , args)
await manager.disconnectAll ()
const client = await connectStdio ('name' , 'command' , ['args' ])
const client = await connectHTTP ('name' , 'https://server.com' , headers)
Transport types: 'stdio' | 'sse' | 'streamable-http'
Personalization import { analyzeSentiment, classifyText, summarize, recommend, createProfile } from '@stacksjs/ai'
const sentiment = await analyzeSentiment ('I love this product!' )
const classification = await classifyText ('Fix the login bug' , ['bug' , 'feature' , 'question' ])
const summary = await summarize (longText, { maxLength : 100 , style : 'bullet' })
const profile = createProfile ('user-123' , ['tech' , 'gaming' ])
recordInteraction (profile, { type : 'view' , itemId : 'article-1' , weight : 1.0 })
const recs = await recommend (profile, contentItems, { limit : 10 })
const interests = await extractUserInterests (profile, items)
Buddy AI Assistant import { processCommand, buddyProcessStreaming, buddyStreamSimple, getAvailableDrivers } from '@stacksjs/ai'
const drivers = getAvailableDrivers ()
const context = await getRepoContext ('/path/to/repo' )
await processCommand ('Add error handling to auth.ts' , 'anthropic' )
for await (const chunk of buddyProcessStreaming ('Refactor this function' , 'openai' , history)) {
process.stdout .write (chunk)
}
Compact Project Context Use the native context contract instead of sending dependency internals, lockfiles,
or an arbitrary repository dump to a coding model:
buddy ai:context
buddy ai:context --json
buddy ai:context --json --output storage/framework/runtime/ai-context.json
buddy ai:context --max-chars 4000 --model claude-sonnet-4
Programmatic callers use buildProjectContext(projectRoot, options). Existing
getRepoContext(projectRoot) calls return its compact text payload. The contract
is deterministic, reports a heuristic token estimate, and describes canonical
Model-View-Action roles, application overrides, instruction files, scripts,
dependency names, and representative application files. It excludes
node_modules, build output, caches, lockfiles, environment files, credentials,
private keys, and secret files by default.
The character budget applies to the prompt payload. JSON adds a versioned metadata
envelope for tools. Token estimates are planning heuristics, not provider billing
counts or evidence that model output is correct.
Claude Agent import { createClaudeLocalAgent, createClaudeEC2Agent } from '@stacksjs/ai'
const agent = createClaudeLocalAgent ({ cwd : '/project' })
for await (const chunk of agent.processCommandStreaming ('Fix the tests' )) {
process.stdout .write (chunk)
}
const remoteAgent = createClaudeEC2Agent ({ ec2Host : '...' , ec2User : '...' , ec2Key : '...' })
Claude Agent SDK import { createClaudeAgentSDKDriver } from '@stacksjs/ai'
const driver = createClaudeAgentSDKDriver ({
maxTurns : 10 , cwd : '/project' , permissionMode : 'auto' ,
allowedTools : ['Read' , 'Write' , 'Edit' , 'Bash' ],
customSystemPrompt : 'You are a Stacks expert'
})
for await (const chunk of driver.processStreaming ('Build a user registration flow' )) {
process.stdout .write (chunk)
}
const sessionId = driver.getLastSessionId ()
await driver.resumeSession (sessionId, 'Add validation' )
config/ai.ts {
default : 'meta.llama2-70b-chat-v1' ,
models : ['meta.llama2-70b-chat-v1' , ...],
deploy : true
}
Gotchas
API keys should be in .env — ANTHROPIC_API_KEY, OPENAI_API_KEY
Ollama requires a local server running on port 11434
Image generation uses OpenAI DALL-E by default
Vision supports both GPT-4V and Claude models
VectorIndex is in-memory — not persisted between restarts
RAG combines chunking + embedding + vector search + LLM generation
MCP supports stdio (subprocess), SSE, and HTTP transports
The buddy assistant has git integration (commit, push, apply changes)
Claude Agent SDK wraps the Claude Code CLI for agentic workflows
Bedrock utilities are for AWS-hosted model invocation
Sentiment/classification use AI models — they're not rule-based
