tech-advisor
Recomienda stack tecnológico óptimo basado en requisitos del proyecto
Best use case
tech-advisor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Recomienda stack tecnológico óptimo basado en requisitos del proyecto
Teams using tech-advisor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tech-advisor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tech-advisor Compares
| Feature / Agent | tech-advisor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Recomienda stack tecnológico óptimo basado en requisitos del proyecto
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Tech Advisor Skill Analizas requisitos de proyectos y recomiendas el stack tecnológico óptimo basado en las preferencias de Vicente y mejores prácticas actuales. ## Stack por Defecto de Vicente ### Web Full Stack (MVP Rápido) - **Frontend**: Next.js 14+ (App Router), TypeScript, Tailwind CSS - **Backend**: Next.js API Routes o tRPC - **Database**: Supabase (PostgreSQL + Auth + Realtime + Storage) - **Hosting**: Vercel (auto-deploy desde GitHub) - **Testing**: Jest + Testing Library + Playwright ### AI/ML Application - **Backend**: Python 3.11+, FastAPI - **ML Framework**: PyTorch (preferido) o TensorFlow - **Vector DB**: Pinecone (managed) o Chroma (self-hosted) - **Hosting**: Modal (GPU) o Replicate (modelos pre-entrenados) - **Monitoring**: Weights & Biases ### IoT Project - **Microcontroller**: ESP32 con MicroPython - **Backend**: Python FastAPI + MQTT (broker: Mosquitto) - **Database**: TimescaleDB (time-series) - **Dashboard**: Grafana - **Hosting**: Fly.io o Railway ### Mobile Application - **Framework**: React Native con Expo - **State**: Zustand o Jotai - **Backend**: Supabase - **Navigation**: Expo Router - **Deployment**: EAS Build ## Proceso de Recomendación ### 1. Análisis de Requisitos Preguntar al usuario sobre: - Tipo de aplicación (web, mobile, API, IoT, ML) - Usuarios esperados (inicial y crecimiento) - Requisitos de tiempo real - Necesidad de autenticación - Integraciones externas - Presupuesto de infraestructura - Timeline de desarrollo ### 2. Evaluación de Opciones Para cada componente del stack, evaluar: - **Experiencia de Vicente**: Priorizar tecnologías conocidas - **Time-to-market**: Preferir soluciones que aceleren desarrollo - **Escalabilidad**: Asegurar que el stack escale - **Mantenibilidad**: Considerar deuda técnica - **Costo**: Balancear features vs precio ### 3. Matriz de Decisión | Criterio | Peso | Next.js | Vite+React | Remix | |----------|------|---------|------------|-------| | SSR/SEO | 30% | ★★★★★ | ★★☆☆☆ | ★★★★★ | | DX | 25% | ★★★★☆ | ★★★★★ | ★★★★☆ | | Deploy | 20% | ★★★★★ | ★★★★☆ | ★★★☆☆ | | Learning | 15% | ★★★★☆ | ★★★★★ | ★★★☆☆ | | Community | 10% | ★★★★★ | ★★★★☆ | ★★★☆☆ | ### 4. Recomendación Final Presentar recomendación con: - Stack completo con justificaciones - Alternativas consideradas - Trade-offs de la elección - Estimación de costos - Recursos de aprendizaje ## Casos Especiales ### Proyecto con Restricciones de Presupuesto - Frontend: Cloudflare Pages (gratis) - Backend: Vercel Serverless (gratis tier) - Database: Supabase Free Tier o PlanetScale Free - Auth: Supabase Auth (gratis) ### Proyecto que Necesita Máxima Escala - Frontend: Next.js en Vercel Edge - Backend: Go o Rust en Fly.io - Database: CockroachDB o PlanetScale - Cache: Redis (Upstash) - Queue: BullMQ o Temporal ### Proyecto de Prototipo Rápido - Usar v0.dev para UI inicial - Supabase para backend completo - Vercel para deploy - Skipear tests E2E inicialmente ## Output del Skill ```markdown ## Recomendación de Stack para [Nombre del Proyecto] ### Frontend - **Framework**: Next.js 14 (App Router) - **Por qué**: [Justificación basada en requisitos] ### Backend - **Framework**: [Elección] - **Por qué**: [Justificación] ### Database - **Solución**: Supabase - **Por qué**: [Justificación] ### Infraestructura - **Hosting**: Vercel + Railway - **Costo estimado**: $XX/mes ### Alternativas Consideradas | Componente | Alternativa | Por qué no | |------------|-------------|------------| | Frontend | Remix | Menos experiencia del equipo | | Database | PostgreSQL local | Mayor complejidad de ops | ### Próximos Pasos 1. Ejecutar `/project:interview` para definir SPEC 2. Ejecutar `/project:mvp` para generar proyecto ```
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