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Senior Fullstack

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yezannnnn
senior-fullstack

Fullstack development toolkit with project scaffolding for Next.js/FastAPI/MERN/Django stacks and code quality analysis. Use when scaffolding new projects, analyzing codebase quality, or implementing fullstack architecture patterns.

Overview

Publisheryezannnnn
RepositoryagentGroup
Skill namesenior-fullstack
Stars
149
Forks
49
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yezannnnn on GitHub. Read the source before you install it.

Installation

Install the Senior Fullstack AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yezannnnn/agentGroup.git /tmp/agentGroup
mkdir -p .claude/skills
cp -r /tmp/agentGroup/jarvis/skills/engineering-team/senior-fullstack .claude/skills/senior-fullstack
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Senior Fullstack in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Senior Fullstack on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Senior Fullstack is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Senior Fullstack

Fullstack development skill with project scaffolding and code quality analysis tools.


Table of Contents


Trigger Phrases

Use this skill when you hear:

  • "scaffold a new project"
  • "create a Next.js app"
  • "set up FastAPI with React"
  • "analyze code quality"
  • "check for security issues in codebase"
  • "what stack should I use"
  • "set up a fullstack project"
  • "generate project boilerplate"

Tools

Project Scaffolder

Generates fullstack project structures with boilerplate code.

Supported Templates:

  • nextjs - Next.js 14+ with App Router, TypeScript, Tailwind CSS
  • fastapi-react - FastAPI backend + React frontend + PostgreSQL
  • mern - MongoDB, Express, React, Node.js with TypeScript
  • django-react - Django REST Framework + React frontend

Usage:

bash
# List available templates
python scripts/project_scaffolder.py --list-templates

# Create Next.js project
python scripts/project_scaffolder.py nextjs my-app

# Create FastAPI + React project
python scripts/project_scaffolder.py fastapi-react my-api

# Create MERN stack project
python scripts/project_scaffolder.py mern my-project

# Create Django + React project
python scripts/project_scaffolder.py django-react my-app

# Specify output directory
python scripts/project_scaffolder.py nextjs my-app --output ./projects

# JSON output
python scripts/project_scaffolder.py nextjs my-app --json

Parameters:

ParameterDescription
templateTemplate name (nextjs, fastapi-react, mern, django-react)
project_nameName for the new project directory
--output, -oOutput directory (default: current directory)
--list-templates, -lList all available templates
--jsonOutput in JSON format

Output includes:

  • Project structure with all necessary files
  • Package configurations (package.json, requirements.txt)
  • TypeScript configuration
  • Docker and docker-compose setup
  • Environment file templates
  • Next steps for running the project

Code Quality Analyzer

Analyzes fullstack codebases for quality issues.

Analysis Categories:

  • Security vulnerabilities (hardcoded secrets, injection risks)
  • Code complexity metrics (cyclomatic complexity, nesting depth)
  • Dependency health (outdated packages, known CVEs)
  • Test coverage estimation
  • Documentation quality

Usage:

bash
# Analyze current directory
python scripts/code_quality_analyzer.py .

# Analyze specific project
python scripts/code_quality_analyzer.py /path/to/project

# Verbose output with detailed findings
python scripts/code_quality_analyzer.py . --verbose

# JSON output
python scripts/code_quality_analyzer.py . --json

# Save report to file
python scripts/code_quality_analyzer.py . --output report.json

Parameters:

ParameterDescription
project_pathPath to project directory (default: current directory)
--verbose, -vShow detailed findings
--jsonOutput in JSON format
--output, -oWrite report to file

Output includes:

  • Overall score (0-100) with letter grade
  • Security issues by severity (critical, high, medium, low)
  • High complexity files
  • Vulnerable dependencies with CVE references
  • Test coverage estimate
  • Documentation completeness
  • Prioritized recommendations

Sample Output:

============================================================
CODE QUALITY ANALYSIS REPORT
============================================================

Overall Score: 75/100 (Grade: C)
Files Analyzed: 45
Total Lines: 12,500

--- SECURITY ---
  Critical: 1
  High: 2
  Medium: 5

--- COMPLEXITY ---
  Average Complexity: 8.5
  High Complexity Files: 3

--- RECOMMENDATIONS ---
1. [P0] SECURITY
   Issue: Potential hardcoded secret detected
   Action: Remove or secure sensitive data at line 42

Workflows

Workflow 1: Start New Project

  1. Choose appropriate stack based on requirements
  2. Scaffold project structure
  3. Run initial quality check
  4. Set up development environment
bash
# 1. Scaffold project
python scripts/project_scaffolder.py nextjs my-saas-app

# 2. Navigate and install
cd my-saas-app
npm install

# 3. Configure environment
cp .env.example .env.local

# 4. Run quality check
python ../scripts/code_quality_analyzer.py .

# 5. Start development
npm run dev

Workflow 2: Audit Existing Codebase

  1. Run code quality analysis
  2. Review security findings
  3. Address critical issues first
  4. Plan improvements
bash
# 1. Full analysis
python scripts/code_quality_analyzer.py /path/to/project --verbose

# 2. Generate detailed report
python scripts/code_quality_analyzer.py /path/to/project --json --output audit.json

# 3. Address P0 issues immediately
# 4. Create tickets for P1/P2 issues

Workflow 3: Stack Selection

Use the tech stack guide to evaluate options:

  1. SEO Required? → Next.js with SSR
  2. API-heavy backend? → Separate FastAPI or NestJS
  3. Real-time features? → Add WebSocket layer
  4. Team expertise → Match stack to team skills

See references/tech_stack_guide.md for detailed comparison.


Reference Guides

Architecture Patterns (references/architecture_patterns.md)

  • Frontend component architecture (Atomic Design, Container/Presentational)
  • Backend patterns (Clean Architecture, Repository Pattern)
  • API design (REST conventions, GraphQL schema design)
  • Database patterns (connection pooling, transactions, read replicas)
  • Caching strategies (cache-aside, HTTP cache headers)
  • Authentication architecture (JWT + refresh tokens, sessions)

Development Workflows (references/development_workflows.md)

  • Local development setup (Docker Compose, environment config)
  • Git workflows (trunk-based, conventional commits)
  • CI/CD pipelines (GitHub Actions examples)
  • Testing strategies (unit, integration, E2E)
  • Code review process (PR templates, checklists)
  • Deployment strategies (blue-green, canary, feature flags)
  • Monitoring and observability (logging, metrics, health checks)

Tech Stack Guide (references/tech_stack_guide.md)

  • Frontend frameworks comparison (Next.js, React+Vite, Vue)
  • Backend frameworks (Express, Fastify, NestJS, FastAPI, Django)
  • Database selection (PostgreSQL, MongoDB, Redis)
  • ORMs (Prisma, Drizzle, SQLAlchemy)
  • Authentication solutions (Auth.js, Clerk, custom JWT)
  • Deployment platforms (Vercel, Railway, AWS)
  • Stack recommendations by use case (MVP, SaaS, Enterprise)

Quick Reference

Stack Decision Matrix

RequirementRecommendation
SEO-critical siteNext.js with SSR
Internal dashboardReact + Vite
API-first backendFastAPI or Fastify
Enterprise scaleNestJS + PostgreSQL
Rapid prototypeNext.js API routes
Document-heavy dataMongoDB
Complex queriesPostgreSQL

Common Issues

IssueSolution
N+1 queriesUse DataLoader or eager loading
Slow buildsCheck bundle size, lazy load
Auth complexityUse Auth.js or Clerk
Type errorsEnable strict mode in tsconfig
CORS issuesConfigure middleware properly

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Senior Fullstack AI skill do?

Fullstack development toolkit with project scaffolding for Next.js/FastAPI/MERN/Django stacks and code quality analysis. Use when scaffolding new projects, analyzing codebase quality, or implementing fullstack architecture patterns.

Why use Senior Fullstack on TypingMind?

Because you install it once and use it with any model. Senior Fullstack is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Senior Fullstack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yezannnnn/agentGroup/tree/master/jarvis/skills/engineering-team/senior-fullstack. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Senior Fullstack?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Senior Fullstack?

As many as you like. As long as a model supports skills, you can use Senior Fullstack with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Senior Fullstack AI skill free?

Yes. It is published on GitHub by yezannnnn under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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