senior-fullstack

name

senior-fullstack

description

Fullstack development toolkit with project scaffolding for Next.js, FastAPI, MERN, and Django stacks, code quality analysis with security and complexity scoring, and stack selection guidance. Use when the user asks to "scaffold a new project", "create a Next.js app", "set up FastAPI with React", "analyze code quality", "audit my codebase", "what stack should I use", "generate project boilerplate", or mentions fullstack development, project setup, or tech stack comparison.

Senior Fullstack

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

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Table of Contents

• Trigger Phrases
• Tools
• Workflows
• Reference Guides

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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"

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Tools

Decision Engine

Deterministic profile picker. Given four assumptions (team-size, cadence, user-facing, budget) plus optional traffic/sensitivity inputs, ranks the four built-in profiles and returns the matched profile with SLO floor and named approver chain. Refuses to recommend a profile without the four required inputs.

Usage:

# See all options
python scripts/fullstack_decision_engine.py --help

# Run against a sample input
python scripts/fullstack_decision_engine.py --sample

# Pick a profile from real inputs
python scripts/fullstack_decision_engine.py \
    --team-size-12mo 8 --cadence daily --user-facing true --budget 5000 \
    --traffic-p99-rps 50 --data-sensitivity pii-only

# JSON output for downstream tools
python scripts/fullstack_decision_engine.py --sample --output json

Returns: matched profile name, score, matched/violated constraints, stack recommendation, anti-recommendations, SLO floor, named-approver chain, and canon references.

The engine encodes the same matrix the conversational grill walks through — use it directly when inputs are already known, or via the cs-fullstack-engineer agent for the question-by-question grill.

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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:

# 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:

Parameter	Description
template	Template name (nextjs, fastapi-react, mern, django-react)
project_name	Name for the new project directory
--output, -o	Output directory (default: current directory)
--list-templates, -l	List all available templates
--json	Output 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

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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:

# 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:

Parameter	Description
project_path	Path to project directory (default: current directory)
--verbose, -v	Show detailed findings
--json	Output in JSON format
--output, -o	Write 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

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Workflows

Workflow 1: Start New Project

1. Choose appropriate stack based on requirements (see Stack Decision Matrix)
2. Scaffold project structure
3. Verify scaffold: confirm package.json (or requirements.txt) exists
4. Run initial quality check — address any P0 issues before proceeding
5. Set up development environment

# 1. Scaffold project
python scripts/project_scaffolder.py nextjs my-saas-app

# 2. Verify scaffold succeeded
ls my-saas-app/package.json

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

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

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

# 6. Start development
npm run dev

Workflow 2: Audit Existing Codebase

1. Run code quality analysis
2. Review security findings — fix all P0 (critical) issues immediately
3. Re-run analyzer to confirm P0 issues are resolved
4. Create tickets for P1/P2 issues

# 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. After fixing P0 issues, re-run to verify
python scripts/code_quality_analyzer.py /path/to/project --verbose

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.

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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)

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Quick Reference

Stack Decision Matrix

Requirement	Recommendation
SEO-critical site	Next.js with SSR
Internal dashboard	React + Vite
API-first backend	FastAPI or Fastify
Enterprise scale	NestJS + PostgreSQL
Rapid prototype	Next.js API routes
Document-heavy data	MongoDB
Complex queries	PostgreSQL

Common Issues

Issue	Solution
N+1 queries	Use DataLoader or eager loading
Slow builds	Check bundle size, lazy load
Auth complexity	Use Auth.js or Clerk
Type errors	Enable strict mode in tsconfig
CORS issues	Configure middleware properly

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Assumptions and Verifiable Success Criteria (Karpathy discipline)

Before this skill scaffolds, recommends, or modifies any code, the following four assumptions MUST be surfaced. If any are unknown, the skill stops and walks the Forcing-question library instead.

1. Team size today + 12-month headcount — drives architecture (monolith / modular / services). Sam Newman: "MonolithFirst."
2. Deployment cadence target — drives CI/CD spend and feature-flag investment. Accelerate (Forsgren et al. 2018).
3. User-facing vs. internal vs. marketing-site — drives stack pick and a11y/perf budget.
4. Monthly cloud + SaaS budget ceiling — drives the build-vs-managed-service split.

Verifiable success criteria (Karpathy #4) — every recommendation this skill emits must include three machine-checkable numbers:

• An API latency target (p50, p95, p99 in ms)
• A frontend perf target (LCP, INP, CLS on mobile-4G)
• An uptime / SLO target

If any of those three is not stated, the recommendation is incomplete — go back to Q7 of the forcing-question library.

The scripts/fullstack_decision_engine.py tool encodes these checks: it refuses to recommend a profile without all four assumption inputs and prints the verifiable thresholds for the matched profile.

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Customization profiles

Four built-in profiles in profiles/ calibrate every recommendation:

Profile	When to pick	Cloud ceiling	Pattern
saas-startup	< 10 eng, customer-facing, daily+ cadence	$8K/mo	Modular monolith on Next.js + Postgres
enterprise-scale	50+ eng, regulated, per-PR with gates	$250K/mo	Domain-bounded services + platform team
internal-tool	≤ 5 eng, auth-walled, < 100 DAU	$500/mo	Retool-first; thin custom stack if forced
marketing-site	SEO-dependent, near-zero write	$200/mo	Static-first (Astro / 11ty / Next-static)

Pick a profile via:

python scripts/fullstack_decision_engine.py \
  --team-size 6 --team-size-12mo 12 \
  --cadence daily --user-facing true --budget 5000 \
  --traffic-p99-rps 45 --data-sensitivity pii-only

The tool returns the best-fit profile, the tradeoff against the runner-up (if within 15%), the stack recommendation, the anti-patterns to avoid on that profile, and the named-approver chain. This tool never auto-approves.

To add a custom profile: copy profiles/saas-startup.json to profiles/<your-org>.json, adjust the constraints and stack_recommendations blocks, and rerun. The JSON is the customization surface — no code changes needed.

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Composition map

This skill does NOT reimplement scope owned by the POWERFUL-tier specialists. It forks into them. See references/composition_map.md for the full routing table. Key forks:

Concern	Fork into
API contract review	engineering/skills/api-design-reviewer/
Database schema design	engineering/skills/database-designer/
Reliability / SLO design	engineering/slo-architect/
CI/CD pipeline	engineering/skills/ci-cd-pipeline-builder/
Performance profiling	engineering/skills/performance-profiler/
Pre-commit Karpathy review	engineering/karpathy-coder/
Pre-flight architecture grill	engineering/grill-me/

The cs-fullstack-engineer agent (in agents/engineering/cs-fullstack-engineer.md) orchestrates these forks via context: fork. Invoke it from another agent with Agent({subagent_type: "cs-fullstack-engineer", prompt: "..."}) or via the slash command /cs:fullstack-review <your problem>.

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Forcing-question library (Matt Pocock grill)

Before locking any architecture or stack decision, walk the seven forcing questions in references/forcing_questions.md. Each has a recommended answer, canon citation, and kill criterion. The discipline:

1. One question per turn. No bundling.
2. Always recommend the answer with cited canon.
3. Track answers in a working file (e.g., /tmp/fullstack-grill-<date>.md).
4. If a kill criterion trips, stop. Do not scaffold around an unresolved gap.
5. After Q7, run fullstack_decision_engine.py with the seven answers as inputs.

Summary of the seven questions (full content in the reference):

1. Team size today + 12-month headcount?
2. Deployment cadence — per-PR, daily, weekly, quarterly?
3. Customer-facing, internal tool, or marketing site?
4. One-year p50 / p99 traffic forecast?
5. Hiring against the stack or training the team?
6. Year-one monthly cloud + SaaS ceiling?
7. Three verifiable success criteria with numeric targets?

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Invocation from other agents and skills

This skill is invokable by any other agent or skill via three surfaces:

1. Slash command: /cs:fullstack-review <prompt> — runs the full grill + decision engine + composition routing.
2. Agent subagent: Agent({subagent_type: "cs-fullstack-engineer", prompt: "..."}) — forks context, returns ≤ 200-word digest.
3. Direct tool call: python scripts/fullstack_decision_engine.py ... — deterministic profile match without the conversational grill (use when inputs are already known).

See agents/engineering/cs-fullstack-engineer.md for the full invocation contract.