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. Table of Contents Trigger Phrases Tools Workflows Reference Guides 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 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. 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 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 Workflows Workflow 1: Start New Project Choose appropriate stack based on requirements (see Stack Decision Matrix) Scaffold project structure Verify scaffold: confirm package.json (or requirements.txt ) exists Run initial quality check — address any P0 issues before proceeding 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 Run code quality analysis Review security findings — fix all P0 (critical) issues immediately Re-run analyzer to confirm P0 issues are resolved 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: SEO Required? → Next.js with SSR API-heavy backend? → Separate FastAPI or NestJS Real-time features? → Add WebSocket layer 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 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 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. Team size today + 12-month headcount — drives architecture (monolith / modular / services). Sam Newman: "MonolithFirst." Deployment cadence target — drives CI/CD spend and feature-flag investment. Accelerate (Forsgren et al. 2018). User-facing vs. internal vs. marketing-site — drives stack pick and a11y/perf budget. 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. 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. 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> . 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: One question per turn. No bundling. Always recommend the answer with cited canon. Track answers in a working file (e.g., /tmp/fullstack-grill-<date>.md ). If a kill criterion trips, stop. Do not scaffold around an unresolved gap. After Q7, run fullstack_decision_engine.py with the seven answers as inputs. Summary of the seven questions (full content in the reference): Team size today + 12-month headcount? Deployment cadence — per-PR, daily, weekly, quarterly? Customer-facing, internal tool, or marketing site? One-year p50 / p99 traffic forecast? Hiring against the stack or training the team?