陌讯skills聚合平台

tech-stack-recommender

📁 alirezarezvani/claude-cto-team 📅 Jan 28, 2026
2
总安装量
2
周安装量
#64269
全站排名
安装命令
npx skills add https://github.com/alirezarezvani/claude-cto-team --skill tech-stack-recommender

Agent 安装分布

mcpjam 2
roo 2
kilo 2
claude-code 2
junie 2
windsurf 2

Skill 文档

Tech Stack Recommender

Provides structured recommendations for technology stack selection based on project requirements, team constraints, and business goals.

When to Use

  • Starting a new project and need stack recommendations
  • Evaluating technology options for specific use cases
  • Comparing frameworks or languages for a project
  • Assessing team readiness for a technology choice
  • Planning technology migrations

Stack Selection Framework

Decision Inputs

┌───────────────────────────────────────────────────────────────────┐
│                    STACK SELECTION INPUTS                         │
├───────────────────────────────────────────────────────────────────┤
│                                                                   │
│  Project Requirements     Team Factors        Business Constraints│
│  ────────────────────     ────────────        ──────────────────  │
│  • Scale expectations     • Current skills    • Time to market    │
│  • Performance needs      • Learning capacity • Budget            │
│  • Integration points     • Team size         • Hiring market     │
│  • Compliance/Security    • Experience level  • Long-term support │
│                                                                   │
└───────────────────────────────────────────────────────────────────┘
                              │
                              ▼
                    ┌─────────────────┐
                    │ RECOMMENDATION  │
                    │   Framework     │
                    └─────────────────┘

Quick Stack Recommendations

By Project Type

Project Type Frontend Backend Database Why
SaaS MVP Next.js Node.js/Express PostgreSQL Fast iteration, full-stack JS
E-commerce Next.js Node.js or Python PostgreSQL + Redis SEO, caching, transactions
Mobile App React Native Node.js/Python PostgreSQL Cross-platform, shared logic
Real-time App React Node.js + WebSocket PostgreSQL + Redis Event-driven, low latency
Data Platform React Python/FastAPI PostgreSQL + ClickHouse Data processing, analytics
Enterprise React Java/Spring or .NET PostgreSQL/Oracle Stability, enterprise support
ML Product React Python/FastAPI PostgreSQL + Vector DB ML ecosystem, inference

By Team Profile

Team Profile Recommended Stack Avoid
Full-stack JS Next.js, Node.js, PostgreSQL Go, Rust (learning curve)
Python Background FastAPI, React, PostgreSQL Heavy frontend frameworks
Enterprise Java Spring Boot, React, PostgreSQL Bleeding-edge tech
Startup (Speed) Next.js, Supabase/Firebase Complex microservices
Scale-Up React, Go/Node, PostgreSQL Monolithic frameworks

Technology Comparison Tables

Frontend Frameworks

Framework Best For Learning Curve Ecosystem Hiring
React Complex UIs, SPAs Medium Excellent Easy
Next.js Full-stack, SSR, SEO Medium Excellent Easy
Vue.js Simpler apps, gradual adoption Easy Good Medium
Svelte Performance-critical Easy Growing Hard
Angular Enterprise, large teams Hard Good Medium

React vs Vue vs Angular

                Speed to MVP    Long-term Maint    Enterprise Ready
React           ████████░░      ████████░░         █████████░
Vue             █████████░      ███████░░          ██████░░░░
Angular         ██████░░░░      █████████░         ██████████

Backend Frameworks

Framework Language Best For Performance Ecosystem
Express Node.js APIs, real-time Good Excellent
Fastify Node.js High-performance APIs Excellent Good
FastAPI Python ML APIs, async Excellent Good
Django Python Full-featured apps Good Excellent
Spring Boot Java Enterprise Good Excellent
Go (Gin/Echo) Go High performance Excellent Good
Rails Ruby Rapid prototyping Moderate Good
NestJS TypeScript Structured Node apps Good Good

When to Use What

## Node.js (Express/Fastify/NestJS)
✅ Real-time applications (WebSocket)
✅ I/O-heavy workloads
✅ Full-stack JavaScript teams
✅ Microservices
❌ CPU-intensive tasks
❌ Heavy computation

## Python (FastAPI/Django)
✅ ML/Data Science integration
✅ Rapid prototyping
✅ Data processing pipelines
✅ Scientific computing
❌ High-concurrency I/O
❌ Real-time systems

## Go
✅ High-performance services
✅ System programming
✅ Concurrent workloads
✅ Microservices at scale
❌ Rapid prototyping
❌ Complex ORM needs

## Java (Spring Boot)
✅ Enterprise applications
✅ Complex business logic
✅ Transaction-heavy systems
✅ Large teams
❌ Quick MVPs
❌ Small projects

Databases

Database Type Best For Scale Complexity
PostgreSQL Relational General purpose, ACID High Medium
MySQL Relational Web apps, read-heavy High Low
MongoDB Document Flexible schemas, JSON High Low
Redis Key-Value Caching, sessions Very High Low
Elasticsearch Search Full-text search High Medium
ClickHouse Columnar Analytics, time-series Very High Medium
DynamoDB Key-Value Serverless, AWS Very High Medium
Cassandra Wide-column Write-heavy, distributed Very High High

Database Selection Guide

Need ACID transactions?
├── YES → PostgreSQL
│
└── NO → What's your primary use case?
    ├── General purpose → PostgreSQL (still!)
    ├── Document storage → MongoDB
    ├── Caching → Redis
    ├── Search → Elasticsearch
    ├── Analytics → ClickHouse/BigQuery
    ├── Time-series → TimescaleDB/InfluxDB
    └── Key-value at scale → DynamoDB/Cassandra

Infrastructure

Platform Best For Complexity Cost
Vercel Next.js, frontend Very Low $ – $$
Railway Simple deployments Low $ – $$
Render General apps Low $ – $$
AWS Everything, scale High $ – $$$$
GCP ML/Data, Kubernetes High $ – $$$$
Azure Enterprise, .NET High $ – $$$$
DigitalOcean Simple, affordable Low $
Fly.io Edge, global Medium $ – $$

Stack Templates

Template 1: Modern SaaS Startup

┌──────────────────────────────────────────────────────────────────┐
│                     MODERN SAAS STACK                            │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            DATABASE                   │
│  ─────────         ───────            ────────                   │
│  Next.js 14        Node.js/Express    PostgreSQL                 │
│  TypeScript        TypeScript         Prisma ORM                 │
│  Tailwind CSS      REST/GraphQL       Redis (cache)              │
│                                                                  │
│  INFRASTRUCTURE    AUTH               PAYMENTS                   │
│  ──────────────    ────               ────────                   │
│  Vercel            Clerk/Auth0        Stripe                     │
│  AWS S3            NextAuth           Stripe Billing             │
│  Cloudflare CDN                                                  │
│                                                                  │
│  MONITORING        CI/CD              ANALYTICS                  │
│  ──────────        ─────              ─────────                  │
│  Sentry            GitHub Actions     PostHog/Amplitude          │
│  Datadog           Vercel Preview     Mixpanel                   │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: B2B SaaS, 0-1M users
Team size: 2-10 engineers
Time to MVP: 4-8 weeks

Template 2: E-Commerce Platform

┌──────────────────────────────────────────────────────────────────┐
│                   E-COMMERCE STACK                               │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            DATABASE                   │
│  ─────────         ───────            ────────                   │
│  Next.js (SSR)     Node.js/Python     PostgreSQL                 │
│  TypeScript        GraphQL/REST       Redis                      │
│  Tailwind/Styled   Medusa/Custom      Elasticsearch              │
│                                                                  │
│  PAYMENTS          SHIPPING           INVENTORY                  │
│  ────────          ────────           ─────────                  │
│  Stripe            ShipStation        Custom/ERP                 │
│  PayPal            EasyPost           Webhook sync               │
│                                                                  │
│  CDN               SEARCH             QUEUE                      │
│  ───               ──────             ─────                      │
│  CloudFront        Algolia/Elastic    SQS/BullMQ                 │
│  Cloudflare        Typesense          Redis                      │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: D2C, Marketplace
Team size: 5-20 engineers
Time to MVP: 8-16 weeks

Template 3: ML-Powered Product

┌──────────────────────────────────────────────────────────────────┐
│                    ML PRODUCT STACK                              │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          API                ML SERVING                 │
│  ─────────         ───                ──────────                 │
│  React/Next.js     FastAPI            TorchServe/Triton          │
│  TypeScript        Python             Docker/K8s                 │
│                    Pydantic           ONNX Runtime               │
│                                                                  │
│  DATABASE          VECTOR DB          FEATURE STORE              │
│  ────────          ─────────          ─────────────              │
│  PostgreSQL        Pinecone           Feast                      │
│  Redis             Weaviate           Redis                      │
│                    pgvector                                      │
│                                                                  │
│  ML OPS            TRAINING           MONITORING                 │
│  ─────             ────────           ──────────                 │
│  MLflow            SageMaker          Weights & Biases           │
│  Airflow           Vertex AI          Prometheus/Grafana         │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: AI products, recommendation systems
Team size: 5-15 engineers + ML team
Time to MVP: 12-24 weeks

Template 4: Real-Time Application

┌──────────────────────────────────────────────────────────────────┐
│                   REAL-TIME STACK                                │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            REAL-TIME                  │
│  ─────────         ───────            ─────────                  │
│  React             Node.js            Socket.io                  │
│  TypeScript        Express/Fastify    WebSocket                  │
│                    TypeScript         Redis Pub/Sub              │
│                                                                  │
│  DATABASE          CACHE              MESSAGE QUEUE              │
│  ────────          ─────              ─────────────              │
│  PostgreSQL        Redis              Redis Streams              │
│  Prisma            In-memory          Kafka (scale)              │
│                                                                  │
│  PRESENCE          STATE SYNC         CONFLICT RESOLUTION        │
│  ────────          ──────────         ───────────────────        │
│  Redis             CRDT/OT            Yjs/Automerge              │
│  Custom            LiveBlocks         Custom                     │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: Chat, collaboration, gaming
Team size: 5-15 engineers
Time to MVP: 8-16 weeks

Technology Trade-off Analysis

Language Selection Matrix

Factor JavaScript/TS Python Go Java Rust
Learning Curve Low Low Medium Medium High
Ecosystem Excellent Excellent Good Excellent Growing
Performance Good Moderate Excellent Good Excellent
Hiring Pool Large Large Medium Large Small
Type Safety TS: Good Optional Excellent Excellent Excellent
Memory Safety GC GC GC GC Compile-time

Framework Selection Criteria

## Evaluation Checklist

1. **Team Expertise** (Weight: 30%)
   - Current skills alignment?
   - Learning curve acceptable?
   - Training resources available?

2. **Project Requirements** (Weight: 30%)
   - Performance requirements met?
   - Feature set complete?
   - Scalability path clear?

3. **Ecosystem** (Weight: 20%)
   - Package availability?
   - Community size?
   - Third-party integrations?

4. **Long-term Viability** (Weight: 20%)
   - Active maintenance?
   - Corporate backing?
   - Future roadmap?

Anti-Patterns to Avoid

Technology Selection Red Flags

Anti-Pattern Why It’s Bad Better Approach
Resume-Driven Choosing tech for career, not project Match to requirements
Hype-Driven Picking latest without evaluation Proven over trendy
Comfort-Only Only familiar tech even when unsuitable Evaluate objectively
Over-Engineering Complex stack for simple needs Start simple
Under-Engineering Simple tools for complex needs Plan for growth

Common Mistakes

❌ "Let's use microservices from day one"
   → Start monolith, extract later

❌ "We need Kubernetes for our 3-person startup"
   → Use managed platforms (Vercel, Railway)

❌ "MongoDB because NoSQL is modern"
   → PostgreSQL handles 95% of use cases better

❌ "GraphQL for everything"
   → REST is simpler for most APIs

❌ "Let's build our own auth"
   → Use Auth0, Clerk, or established solutions

Migration Considerations

When to Consider Migration

Trigger Action
Performance bottlenecks Profile first, then consider
Team expertise mismatch Train or hire before migrating
End of life/support Plan 6-12 months ahead
Scale limitations Validate limits with benchmarks
Security vulnerabilities Patch if possible, migrate if not

Migration Risk Assessment

LOW RISK:
- Library/package updates
- Minor version upgrades
- Adding new services

MEDIUM RISK:
- Database version upgrades
- Framework major versions
- New deployment platform

HIGH RISK:
- Language/framework rewrites
- Database technology changes
- Monolith to microservices

Quick Reference

“I’m building a…”

Project Recommended Stack
Blog/CMS Next.js + Headless CMS (Sanity/Contentful)
SaaS Dashboard Next.js + Node.js + PostgreSQL
Mobile App React Native + Node.js + PostgreSQL
E-commerce Next.js + Medusa/Custom + PostgreSQL
Real-time Chat React + Node.js + Socket.io + Redis
Data Dashboard React + Python/FastAPI + PostgreSQL
ML Product React + Python/FastAPI + PostgreSQL + Vector DB
API Service Node.js or Python + PostgreSQL

Stack Complexity Levels

Complexity Description Example Stack
Minimal Single deployment, managed services Vercel + Supabase
Simple Separate frontend/backend Vercel + Railway + PostgreSQL
Standard Multiple services, caching AWS ECS + RDS + Redis
Complex Microservices, event-driven K8s + Multiple DBs + Kafka

References

GitHub 仓库 ↗ ← 返回陌讯 Skills 聚合平台
本页面由 陌讯 Skills 聚合平台 收录整理，数据定期更新。