Fullstack Dev
HHU3637kr/skills
Full-stack backend architecture and frontend-backend integration guide.
Recommend technology stacks based on project requirements, team expertise, and constraints.
Install Tech Stack Recommender
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommender --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tech-stack-recommender .claude/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .claude/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommenderType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommender --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tech-stack-recommender .agents/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .agents/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommender --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tech-stack-recommender .cursor/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .cursor/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-cto-team.git --path skills/tech-stack-recommender--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommender --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tech-stack-recommender .gemini/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .gemini/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommenderInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tech-stack-recommender .github/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .github/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-cto-team tech-stack-recommender --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-cto-team.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tech-stack-recommender .opencode/skills/tech-stack-recommender && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tech-stack-recommender" agent skill from https://github.com/alirezarezvani/claude-cto-team/tree/main/skills/tech-stack-recommender into .opencode/skills/tech-stack-recommender/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tech-stack-recommender", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
tech-stack-recommender- GitHub stars
- 117
- Token cost
- ~4.3k tokens
- SKILL.md length
- 678 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- MIT
At a glance
Recommend technology stacks based on project requirements, team expertise, and constraints.
- Selecting frameworks
- SKILL.md covers When to Use, Stack Selection Framework, Quick Stack Recommendations and Technology Comparison Tables, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Infrastructure for new projects
What it does
Tech Stack Recommender is an agent skill from alirezarezvani/claude-cto-team. Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.
Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Backend & APIs. It works with React, PostgreSQL, Next.js and Python. The repository describes itself as: Your personal CTO Team for Claude Code . These Subagents will help you challenging yourself while you plan and execute. The licence is MIT.
When your agent uses it
- Selecting frameworks
- Infrastructure for new projects
Example prompts
- “/tech-stack-recommender”
Requirements
- Python 3
- Node.js
What it can do on your machine
Read from SKILL.md and the folder at commit a5bbb78. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Tech Stack Recommender loads about 4.3k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 678 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from alirezarezvani/claude-cto-team at commit a5bbb78, republished under its MIT licence (© alirezarezvani). 678 words, ~4,346 tokens.
Download SKILL.mdSave it as
.claude/skills/tech-stack-recommender/SKILL.md (or your agent's skills folder).- name
- tech-stack-recommender
- description
- Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.
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 projectsDatabases
| 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/CassandraInfrastructure
| 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 weeksShow full SKILL.md (271 more words)Show less
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 weeksTemplate 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 weeksTemplate 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 weeksTechnology 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 solutionsMigration 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 microservicesQuick 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
- Framework Comparison - Detailed feature comparisons
- Migration Playbooks - Step-by-step migration guides
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/tech-stack-recommender of alirezarezvani/claude-cto-team.
Open the folder on GitHubat commit a5bbb78
Compare with similar skills
Tech Stack Recommender next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tech Stack Recommender this skillalirezarezvani/claude-cto-team | 117 | — | ~4.3k | Automated safety check: Pass | MIT | |
| Fullstack DevHHU3637kr/skills | 145 | 3 repos | ~8.6k | Automated safety check: Notes | MIT | |
| Fullstack Devinfometa/workbuddyskills | 344 | — | ~1k | Automated safety check: Pass | MIT | |
| Senior Fullstackdavila7/claude-code-templates | 32k | 7 repos | ~1.1k | Automated safety check: Notes | MIT | |
| Env Managerbobmatnyc/claude-mpm | 155 | — | ~3.9k | Automated safety check: Notes | Custom licence | |
| Posthog SDK Patternsjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.1k | Automated safety check: Pass | MIT |
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curvenote/curvenote
A skill your agent uses when doing ANY task involving Supabase.
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Detect common technical and organizational anti-patterns in proposals, architectures, and plans.
Architecture Pattern Selector
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Assumption Challenger
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Cost Estimator
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Infrastructure and development cost estimation for technical projects.
ML Cv Specialist
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Deep expertise in ML/CV model selection, training pipelines, and inference architecture.
Request Analyzer
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Questions about Tech Stack Recommender
What does Tech Stack Recommender do?
Recommend technology stacks based on project requirements, team expertise, and constraints. Tech Stack Recommender is an agent skill from alirezarezvani/claude-cto-team. Recommend technology stacks based on project requirements, team expertise, and constraints.
When should I use Tech Stack Recommender?
Tech Stack Recommender fits situations like: selecting frameworks; infrastructure for new projects.
How do I install Tech Stack Recommender in Claude Code?
Run `npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a claude-code`. Or copy the skill folder (skills/tech-stack-recommender in alirezarezvani/claude-cto-team) into .claude/skills/tech-stack-recommender in your project. Claude Code loads it when a task matches its description.
How do I install Tech Stack Recommender in Codex?
Run `npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a codex`. Or copy the skill folder (skills/tech-stack-recommender in alirezarezvani/claude-cto-team) into .agents/skills/tech-stack-recommender in your project. Codex loads it when a task matches its description.
Can I use Tech Stack Recommender in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-cto-team --skill tech-stack-recommender -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tech-stack-recommender, .gemini/skills/tech-stack-recommender, .github/skills/tech-stack-recommender and .opencode/skills/tech-stack-recommender in your project.
What does Tech Stack Recommender need to run?
SKILL.md names no scripts, command-line tools or credentials: Tech Stack Recommender is instructions for the agent only. Our summary lists: Python 3; Node.js.
Does Tech Stack Recommender access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Tech Stack Recommender safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Tech Stack Recommender use?
Tech Stack Recommender is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Tech Stack Recommender use?
About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Tech Stack Recommender?
Skills that share tags, products or a category with Tech Stack Recommender: Fullstack Dev (HHU3637kr/skills, 145 stars), Fullstack Dev (infometa/workbuddyskills, 344 stars), Senior Fullstack (davila7/claude-code-templates, 32k stars) and Env Manager (bobmatnyc/claude-mpm, 155 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Tech Stack Recommender?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-cto-team, which has 117 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on December 18, 2025.
Source: alirezarezvani/claude-cto-team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.