techwavedev 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"}],
"samples": [
"{query}",
"me ajuda com {query}",
"quero saber sobre {query}",
"o que voce sabe sobre {query}",
"explique {query}",
"pesquise {query}"
]
},
{
"name": "SmartHomeIntent",
"slots": [
{"name": "device", "type": "AMAZON.Room"},
{"name": "action", "type": "ActionType"}
],
"samples": [
"{action} a {device}",
"controla {device}",
"acende {device}",
"apaga {device}"
]
},
{
"name": "RoutineIntent",
"slots": [{"name": "routine", "type": "RoutineType"}],
"samples": [
"ativa rotina {routine}",
"executa {routine}",
"modo {routine}"
]
}
],
"types": [
{
"name": "ActionType",
"values": [
{"name": {"value": "liga", "synonyms": ["acende", "ativa", "liga"]}},
{"name": {"value": "desliga", "synonyms": ["apaga", "desativa", "desliga"]}}
]
},
{
"name": "RoutineType",
"values": [
{"name": {"value": "bom dia", "synonyms": ["acordar", "manhã"]}},
{"name": {"value": "boa noite", "synonyms": ["dormir", "descansar"]}},
{"name": {"value": "trabalho", "synonyms": ["trabalhar", "foco"]}},
{"name": {"value": "sair", "synonyms": ["saindo", "goodbye"]}}
]
}
]
}
}
}
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") else None
if not query:
return (handler_input.response_builder
.speak("Pode repetir? Nao entendi bem.").ask("Pode repetir?").response)
# Carregar historico
attrs = handler_input.attributes_manager.persistent_attributes
history = attrs.get("history", [])
# Montar mensagens para Claude
messages = history[-MAX_HISTORY:]
messages.append({"role": "user", "content": query})
# Chamar Claude
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
response = client.messages.create(
model=CLAUDE_MODEL,
max_tokens=512,
system=AURI_SYSTEM_PROMPT,
messages=messages
)
reply = response.content[0].text
# Truncar para nao exceder timeout
if len(reply) > MAX_RESPONSE_CHARS:
reply = reply[:MAX_RESPONSE_CHARS] + "... Quer que eu continue?"
# Salvar historico
history.append({"role": "user", "content": query})
history.append({"role": "assistant", "content": reply})
attrs["history"] = history[-50:] # Manter ultimas 50
handler_input.attributes_manager.persistent_attributes = attrs
handler_input.attributes_manager.save_persist
## 4.2 Variaveis De Ambiente Lambda
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": {}
},
"updatedAt": 1740960000,
"ttl": 1748736000
}
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",
"color": "#e94560",
"textAlign": "center",
"paddingTop": "20px"
},
{
"type": "Text",
"text": "${payload.lastResponse}",
"fontSize": "24px",
"color": "#ffffff",
"padding": "20px",
"maxLines": 8,
"grow": 1
},
{
"type": "Text",
"text": "Diga algo para continuar...",
"fontSize": "18px",
"color": "#888888",
"textAlign": "center",
"paddingBottom": "20px"
}
]
}]
}
}
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
---
<!-- AGI-INTEGRATION-START -->
## AGI Framework Integration
> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)
### Memory-First Protocol
Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.
```bash
# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Amazon Alexa"
Storing Results
After completing work, store infrastructure decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
--type technical --project <project> \
--tags amazon-alexa devops
Multi-Agent Collaboration
Broadcast deployment changes so frontend and backend agents update their configurations accordingly.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
--project <project>
Playbook Integration
Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.