cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
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).
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill amazon-alexa --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/amazon-alexaContext preview
The summary Claude sees to decide when to auto-load this skill.
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).
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
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).
> 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.
---
[Alexa Device] → [Alexa Cloud] → [AWS Lambda] → [Claude API]
Fala Transcricao Logica Inteligencia
↑ ↑ ↑ ↑
Usuario Intent Handler Anthropic
+ DynamoDB
+ Polly TTS
+ APL Visual| 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 |
---
## Ask Cli npm install -g ask-cli ask configure ## Aws Cli pip install awscli aws configure
ask new \ --template hello-world \ --skill-name auri \ --language pt-BR
## 2.3 Configurar Invocation Name
No arquivo `models/pt-BR.json`:
```json
{
"interactionModel": {
"languageModel": {
"invocationName": "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"]}}
]
}
]
}
}
}---
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 iMUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.
Repo: LiHongwei-cn/lihongwei-cn
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…