amazon-alexa

name	amazon-alexa
description	Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
risk	safe
source	community
date_added	2026-03-06
author	renat
tags	["voice","alexa","aws","smart-home","iot"]
tools	["claude-code","antigravity","cursor","gemini-cli","codex-cli"]

AMAZON ALEXA — Voz Inteligente com Claude

Overview

Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).

When to Use This Skill

• When you need specialized assistance with this domain

Do Not Use This Skill When

• The task is unrelated to amazon alexa
• A simpler, more specific tool can handle the request
• The user needs general-purpose assistance without domain expertise

How It Works

Voce e o especialista em Alexa e AWS Voice. Missao: transformar qualquer dispositivo Alexa em assistente ultra-inteligente usando Claude como LLM backend, com voz neural, memoria persistente e controle de Smart Home. Projeto-chave: AURI.

---

1. Visao Geral Do Ecossistema

[Alexa Device] → [Alexa Cloud] → [AWS Lambda] → [Claude API]
    Fala          Transcricao      Logica          Inteligencia
      ↑               ↑               ↑                ↑
   Usuario         Intent        Handler          Anthropic
                               + DynamoDB
                               + Polly TTS
                               + APL Visual

Componentes Da Arquitetura Auri

Componente	Servico AWS	Funcao
Voz → Texto	Alexa ASR nativo	Reconhecimento de fala
NLU	ASK Interaction Model + Lex V2	Extrair intent e slots
Backend	AWS Lambda (Python/Node.js)	Logica e orquestracao
LLM	Claude API (Anthropic)	Inteligencia e respostas
Persistencia	Amazon DynamoDB	Historico e preferencias
Texto → Voz	Amazon Polly (neural)	Fala natural da Auri
Interface Visual	APL (Alexa Presentation Language)	Telas em Echo Show
Smart Home	Alexa Smart Home API	Controle de dispositivos
Automacao	Alexa Routines API	Rotinas inteligentes

---

2.1 Pre-Requisitos

## Ask Cli

npm install -g ask-cli
ask configure

## Aws Cli

pip install awscli
aws configure

Criar Skill Com Template

ask new
--template hello-world
--skill-name auri
--language pt-BR

└── .Ask/Ask-Resources.Json

## 2.3 Configurar Invocation Name

No arquivo `models/pt-BR.json`:
```json
{
  "interactionModel": {
    "languageModel": {
      "invocationName": "auri"
    }
  }
}

---

3.1 Intents Essenciais Para Auri

{
  "interactionModel": {
    "languageModel": {
      "invocationName": "auri",
      "intents": [
        {"name": "AMAZON.HelpIntent"},
        {"name": "AMAZON.StopIntent"},
        {"name": "AMAZON.CancelIntent"},
        {"name": "AMAZON.FallbackIntent"},
        {
          "name": "ChatIntent",
          "slots": [{"name": "query", "type": "AMAZON.SearchQuery"}],
           
            
            
            
            
            
            
          
        
        
           
           
               
               
          
           
            
            
            
            
          
        
        
           
              
           
            
            
            
          
        
      
       
        
           
           
                  
                  
          
        
        
           
           
                 
                 
                 
                 
          
        
      
    
  

---

4.1 Handler Principal Python

import os
import time
import anthropic
import boto3
from ask_sdk_core.skill_builder import SkillBuilder
from ask_sdk_core.handler_input import HandlerInput
from ask_sdk_core.utils import is_intent_name, is_request_type
from ask_sdk_model import Response
from ask_sdk_dynamodb_persistence_adapter import DynamoDbPersistenceAdapter

## ============================================================

@sb.request_handler(can_handle_func=is_request_type("LaunchRequest"))
def launch_handler(handler_input: HandlerInput) -> Response:
    attrs = handler_input.attributes_manager.persistent_attributes
    name = attrs.get("name", "")
    greeting = f"Oi{', ' + name if name else ''}! Eu sou a Auri. Como posso ajudar?"
    return (handler_input.response_builder
            .speak(greeting).ask("Em que posso ajudar?").response)


@sb.request_handler(can_handle_func=is_intent_name("ChatIntent"))
def chat_handler(handler_input: HandlerInput) -> Response:
    try:
        # Obter query
        slots = handler_input.request_envelope.request.intent.slots
        query = slots["query"].value if slots.get("query")  
          query:
             (handler_input.response_builder
                    .speak().ask().response)

        
        attrs = handler_input.attributes_manager.persistent_attributes
        history = attrs.get(, [])

        
        messages = history[-MAX_HISTORY:]
        messages.append({: , : query})

        
        client = anthropic.Anthropic(api_key=os.enprojectn[])
        response = client.messages.create(
            model=CLAUDE_MODEL,
            max_tokens=,
            system=AURI_SYSTEM_PROMPT,
            messages=messages
        )
        reply = response.content[].text

        
         (reply) > MAX_RESPONSE_CHARS:
            reply = reply[:MAX_RESPONSE_CHARS] + 

        
        history.append({: , : query})
        history.append({: , : reply})
        attrs[] = history[-:]  
        handler_input.attributes_manager.persistent_attributes = attrs
        handler_input.attributes_manager.save_persist

ANTHROPIC_API_KEY=sk-... (armazenar em Secrets Manager) DYNAMODB_TABLE=auri-users AWS_REGION=us-east-1

### 4.3 Requirements.Txt

ask-sdk-core>=1.19.0 ask-sdk-dynamodb-persistence-adapter>=1.19.0 anthropic>=0.40.0 boto3>=1.34.0

---

### 5.1 Criar Tabela

```bash
aws dynamodb create-table \
  --table-name auri-users \
  --attribute-definitions AttributeName=userId,AttributeType=S \
  --key-schema AttributeName=userId,KeyType=HASH \
  --billing-mode PAY_PER_REQUEST \
  --region us-east-1

5.2 Schema Do Usuario

{
  "userId": "amzn1.ask.account.XXXXX",
  "name": "Joao",
  "history": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ],
  "preferences": {
    "language": "pt-BR",
    "voice": "Vitoria",
    "personality": "assistente profissional"
  },
  "smartHome": {
    "devices": {},
    "routines": 
  
   
   

5.3 Ttl Automatico (Expirar Dados Antigos)

import time

## Adicionar Ttl De 180 Dias Ao Salvar

attrs["ttl"] = int(time.time()) + (180 * 24 * 3600)

---

6.1 Vozes Disponiveis (Portugues)

Voice	Idioma	Tipo	Recomendado
Vitoria	pt-BR	Neural	✅ Auri PT-BR
Camila	pt-BR	Neural	Alternativa
Ricardo	pt-BR	Standard	Masculino
Ines	pt-PT	Neural	Portugal

6.2 Integrar Polly Na Resposta

import boto3
import base64

def synthesize_polly(text: str, voice_id: str = "Vitoria") -> str:
    """Retorna URL de audio Polly para usar em Alexa."""
    client = boto3.client("polly", region_name="us-east-1")
    response = client.synthesize_speech(
        Text=text,
        OutputFormat="mp3",
        VoiceId=voice_id,
        Engine="neural"
    )
    # Salvar em S3 e retornar URL
    # (necessario para usar audio customizado no Alexa)
    return upload_to_s3(response["AudioStream"].read())

def speak_with_polly(handler_input, text, voice_id="Vitoria"):
    """Retornar resposta usando voz Polly customizada via SSML."""
    audio_url = synthesize_polly(text, voice_id)
    ssml = f'<speak><audio src="{audio_url}"/></speak>'
    return handler_input.response_builder.speak(ssml)

6.3 Ssml Para Controle De Voz

<speak>
  <prosody rate="90%" pitch="+5%">
    Oi! Eu sou a Auri.
  </prosody>
  <break time="0.5s"/>
  <emphasis level="moderate">Como posso ajudar?</emphasis>
</speak>

---

7.1 Template De Chat

{
  "type": "APL",
  "version": "2023.3",
  "theme": "dark",
  "mainTemplate": {
    "parameters": ["payload"],
    "items": [{
      "type": "Container",
      "width": "100%",
      "height": "100%",
      "backgroundColor": "#1a1a2e",
      "items": [
        {
          "type": "Text",
          "text": "AURI",
          "fontSize": "32px",
           
           
           
        
        
           
           
           
           
           
           
           
        
        
           
           
           
           
           
           
        
      
    
  

7.2 Adicionar Apl Na Resposta

@sb.request_handler(can_handle_func=is_intent_name("ChatIntent"))
def chat_with_apl(handler_input: HandlerInput) -> Response:
    # ... obter reply do Claude ...

    # Verificar se device suporta APL
    supported = handler_input.request_envelope.context.system.device.supported_interfaces
    has_apl = getattr(supported, "alexa_presentation_apl", None) is not None

    if has_apl:
        apl_directive = {
            "type": "Alexa.Presentation.APL.RenderDocument",
            "token": "auri-chat",
            "document": CHAT_APL_DOCUMENT,
            "datasources": {"payload": {"lastResponse": reply}}
        }
        handler_input.response_builder.add_directive(apl_directive)

    return handler_input.response_builder.speak(reply).ask("Mais alguma coisa?").response

---

8.1 Ativar Smart Home Skill

No skill.json, adicionar:

{
  "apis": {
    "smartHome": {
      "endpoint": {
        "uri": "arn:aws:lambda:us-east-1:123456789:function:auri-smart-home"
      }
    }
  }
}

8.2 Handler De Smart Home

def handle_smart_home_directive(event, context):
    namespace = event["directive"]["header"]["namespace"]
    name = event["directive"]["header"]["name"]
    endpoint_id = event["directive"]["endpoint"]["endpointId"]

    if namespace == "Alexa.PowerController":
        state = "ON" if name == "TurnOn" else "OFF"
        # Chamar sua API de smart home
        control_device(endpoint_id, {"power": state})
        return build_smart_home_response(endpoint_id, "powerState", state)

    elif namespace == "Alexa.BrightnessController":
        brightness = event["directive"]["payload"]["brightness"]
        control_device(endpoint_id, {"brightness": brightness})
        return build_smart_home_response(endpoint_id, "brightness", brightness)

8.3 Discovery De Dispositivos

def handle_discovery(event, context):
    return {
        "event": {
            "header": {
                "namespace": "Alexa.Discovery",
                "name": "Discover.Response",
                "payloadVersion": "3"
            },
            "payload": {
                "endpoints": [
                    {
                        "endpointId": "light-sala-001",
                        "friendlyName": "Luz da Sala",
                        "displayCategories": ["LIGHT"],
                        "capabilities": [
                            {
                                "type": "AlexaInterface",
                                "interface": "Alexa.PowerController",
                                "version": "3"
                            },
                            {
                                "type": "AlexaInterface",
                                "interface": "Alexa.BrightnessController",
                                "version": "3"
                            }
                        ]
                    }
                ]
            }
        }
    }

---

Deploy Completo (Skill + Lambda)

cd auri/ ask deploy

Verificar Status

ask status

Testar No Simulador

ask dialog --locale pt-BR

Teste Especifico De Intent

ask simulate
--text "abrir auri"
--locale pt-BR
--skill-id amzn1.ask.skill.YOUR-SKILL-ID

## Criar Lambda Manualmente

aws lambda create-function \
  --function-name auri-skill \
  --runtime python3.11 \
  --role arn:aws:iam::ACCOUNT:role/auri-lambda-role \
  --handler lambda_function.handler \
  --timeout 8 \
  --memory-size 512 \
  --zip-file fileb://function.zip

## Adicionar Trigger Alexa

aws lambda add-permission \
  --function-name auri-skill \
  --statement-id alexa-skill-trigger \
  --action lambda:InvokeFunction \
  --principal alexa-appkit.amazon.com \
  --event-source-token amzn1.ask.skill.YOUR-SKILL-ID

Usar Secrets Manager

aws secretsmanager create-secret
--name auri/anthropic-key
--secret-string '{"ANTHROPIC_API_KEY": "sk-..."}'

Lambda Acessa Via Sdk:

import boto3, json def get_secret(secret_name): client = boto3.client('secretsmanager') response = client.get_secret_value(SecretId=secret_name) return json.loads(response['SecretString'])

---

## Fase 1 — Setup (Dia 1)

[ ] Conta Amazon Developer criada [ ] Conta AWS configurada (free tier) [ ] ASK CLI instalado e configurado [ ] IAM Role criada com permissoes: Lambda, DynamoDB, Polly, Logs [ ] Anthropic API key armazenada em Secrets Manager

## Fase 2 — Skill Base (Dia 2-3)

[ ] ask new --template hello-world --skill-name auri [ ] Interaction model definido (pt-BR.json) [ ] LaunchRequest handler funcionando [ ] ChatIntent handler com Claude integrado [ ] ask deploy funcionando [ ] Teste basico no ASK simulator

## Fase 3 — Persistencia (Dia 4)

[ ] DynamoDB table criada [ ] Persistencia de historico funcionando [ ] TTL configurado [ ] Preferencias do usuario salvas

## Fase 4 — Polly + Apl (Dia 5-6)

[ ] Polly integrado com voz Vitoria (neural) [ ] APL template de chat criado [ ] APL renderizando em Echo Show simulator

## Fase 5 — Smart Home (Opcional)

[ ] Smart Home skill habilitada [ ] Discovery de dispositivos funcionando [ ] PowerController implementado [ ] Teste com device real

## Fase 6 — Publicacao

[ ] Teste completo de todas funcionalidades [ ] Performance OK (< 8s timeout) [ ] Certificacao Amazon submetida [ ] Publicado na Alexa Skills Store

---

## 11. Comandos Rapidos

| Acao | Comando |
|------|---------|
| Criar skill | `ask new --template hello-world` |
| Deploy | `ask deploy` |
| Simular | `ask simulate --text "abre a auri"` |
| Dialog interativo | `ask dialog --locale pt-BR` |
| Ver logs | `ask smapi get-skill-simulation` |
| Validar modelo | `ask validate --locales pt-BR` |
| Exportar skill | `ask smapi export-package --skill-id ID` |
| Listar skills | `ask list skills` |

---

## 12. Referencias

- Boilerplate Python completo: `assets/boilerplate/lambda_function.py`
- Interaction model PT-BR: `assets/interaction-models/pt-BR.json`
- APL chat template: `assets/apl-templates/chat-interface.json`
- Smart Home examples: `references/smart-home-api.md`
- ASK SDK Python docs: https://github.com/alexa/alexa-skills-kit-sdk-for-python
- Claude + Alexa guide: https://www.anthropic.com/news/claude-and-alexa-plus

## Best Practices

- Provide clear, specific context about your project and requirements
- Review all suggestions before applying them to production code
- Combine with other complementary skills for comprehensive analysis

## Common Pitfalls

- Using this skill for tasks outside its domain expertise
- Applying recommendations without understanding your specific context
- Not providing enough project context for accurate analysis

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for enprojectnment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.