Agent skill

technical-automation-architect

Design technical architecture and automation strategies for solo SaaS products. Use when selecting tech stacks, deciding build vs buy, or implementing AI automation to scale operations.

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/technical-automation-architect

SKILL.md

When to Use This Skill

Use this skill when you need to:

  • Choose a tech stack for your solo SaaS product
  • Decide build vs buy for features and infrastructure
  • Implement AI automation to multiply your effectiveness
  • Leverage managed services instead of building from scratch
  • Use SaaS boilerplates to accelerate development
  • Manage technical debt strategically as solo founder
  • Scale to $1M ARR as solo or tiny team

Core Concepts

"Boring Stack" Philosophy

Use established technologies, not cutting-edge:

Why boring wins:

  • Faster development (you know the tools)
  • Fewer bugs (battle-tested libraries)
  • Easier hiring (common skills)
  • Better resources (tutorials, help, solutions)
  • Long-term maintainability (won't abandon you)

The cost of new/shiny:

  • Learning curve: 2-3 months to become productive
  • Unknown bugs and edge cases
  • Sparse documentation and community
  • Abandonment risk (if project dies)

Principle: Leverage existing skills over chasing new tech

Build vs Buy: Leverage Everything

The ruthless equation: "Would you rather spend 3 months building authentication or 3 months acquiring your first 100 customers?"

Leverage (Buy) for:

  • Authentication (Auth0, Supabase Auth, Clerk)
  • Payment processing (Stripe, Paddle)
  • Email infrastructure (SendGrid, Resend, Postmark)
  • User management frameworks
  • Managed databases (RDS, Supabase, PlanetScale)
  • Hosting (Vercel, Railway, Fly.io)

Build only:

  • Your core differentiator (unique value proposition)
  • Features where existing solutions don't fit
  • Custom integrations that don't exist

SaaS boilerplates: Can significantly reduce setup time by prebuilding common foundation pieces.

Step-by-Step Architecture Process

Phase 1: Stack Selection (Week 1)

Choose technologies you already know:

  • Backend: Django, Rails, or Go (what you're proficient in)
  • Frontend: React, NextJS, or HTMX (leverage existing skills)
  • Database: PostgreSQL (battle-tested) or SQLite (start simple)
  • Infrastructure: Managed services (don't self-host initially)

Avoid:

  • New languages you'll need to learn
  • Cutting-edge frameworks (use stable, mature tech)
  • Complex architectures (microservices, K8s) until proven need

Deliverable: Tech stack decision document

Phase 2: Build vs Buy Matrix (Week 2)

List all components needed:

  • Authentication, payments, email, database, hosting, etc.

For each component:

  • What managed services exist?
  • Cost of managed service vs build time?
  • Does it integrate well with chosen stack?
  • What's the exit strategy if service fails?

Decision criteria (rules of thumb):

  • Buy if: Managed service integration is clearly faster than custom build
  • Buy if: Not core differentiator
  • Build if: Core unique value prop
  • Build if: Existing solutions don't fit use case

Deliverable: Build/buy decision matrix

Phase 3: SaaS Boilerplate Evaluation (Week 3)

Research boilerplates in your stack:

  • Django: ShipFast, SaaS Pegasus
  • Rails: Jumpstart, Bullet Train
  • NextJS: Supabase SaaS Kit, various Next.js starters

Evaluation criteria:

  • Active maintenance (last commit within 3 months)
  • Community size (stars, issues, discussions)
  • Feature match (high overlap with your immediate roadmap)
  • License terms (MIT vs paid)
  • Tech stack alignment (your preferred tools)

Decision:

  • Use boilerplate: If most foundational needs are covered and code quality is acceptable
  • Build from scratch: If highly custom requirements

Deliverable: Boilerplate choice or scratch-build decision

Phase 4: Automation Planning (Week 4)

Identify automation opportunities:

  • Repetitive tasks (daily/weekly)
  • Manual processes (customer onboarding, reporting)
  • Communication (emails, notifications, updates)
  • Operations (deployments, backups, monitoring)

Automation tools:

  • No-code: Zapier, Make (n8n), Airtable
  • Code: Scripts, GitHub Actions, cron jobs
  • AI: Cursor, v0, Bolt for code generation
  • Infrastructure: Terraform, Docker for reproducibility

Deliverable: Automation roadmap prioritized by ROI

Common Mistakes

Mistake 1: Choosing Shiny Over Familiar

  • Problem: Spend 3 months learning Rust when you know Python
  • Solution: Use boring stack you're proficient in

Mistake 2: Building Solved Problems

  • Problem: 3 months building auth from scratch
  • Solution: Use Auth0/Supabase in 1 day, ship features

Mistake 3: Over-Engineering Early

  • Problem: Microservices, K8s for MVP
  • Solution: Monolith, managed hosting until proven need

Mistake 4: No Automation Strategy

  • Problem: Manual everything, stuck in operations
  • Solution: Automate high-frequency, low-judgment work early

Mistake 5: Ignoring Technical Debt

  • Problem: Accumulate debt unconsciously, drowning in hacks
  • Solution: Conscious trade-offs, scheduled repayment

Success Metrics

Technical Health Indicators (directional targets):

Metric Warning Healthy Optimal
Dev velocity <1 feature/week 2-3 features/week 4-5 features/week
Downtime/month >2 hours <30 minutes <5 minutes
Bugs per release 5+ 1-2 0-1
Deployment frequency Monthly Weekly Daily
Technical debt ratio >40% 20-30% <20%

Red flags:

  • ❌ Taking 2+ weeks to ship simple features
  • ❌ Constant production incidents
  • ❌ Dreading code changes (fear of breaking things)
  • ❌ Can't take time off (product breaks without you)

Deep Dives

For comprehensive technical strategies, tools, and frameworks, see the references:

references/stack-comparison.md

  • Django vs Rails vs Go comparison
  • Frontend options: React vs NextJS vs HTMX
  • Database choices: PostgreSQL vs SQLite vs MySQL
  • Hosting infrastructure options
  • Real-world stack examples from successful solo SaaS

references/managed-services.md

  • Authentication: Auth0, Supabase Auth, Clerk comparison
  • Payments: Stripe vs Paddle configuration
  • Email: SendGrid, Resend, Postmark setup
  • Databases: RDS, Supabase, PlanetScale evaluation
  • Cost-benefit calculations for each service

references/automation-checklist.md

  • 50+ automation opportunities identified
  • No-code tools comparison (Zapier vs Make vs n8n)
  • AI-assisted automation patterns for solo teams
  • Developer productivity multipliers
  • CI/CD pipeline templates

Research Notes

This skill synthesizes findings from technical operations research:

Primary Research:

Key Principles:

  • Boring stack philosophy - Established tech over shiny new tools
  • Leverage everything - Solved problems shouldn't be rebuilt
  • Conscious technical debt - Documented trade-offs, scheduled repayment
  • Ruthless automation - Automate repetitive work to protect focus for core product and customer outcomes

Recommended Stacks:

  • Backend: Django (Python), Rails (Ruby), Go + HTMX + SQLite
  • Frontend: React + SWR, NextJS, or HTMX
  • Database: PostgreSQL (production), SQLite (start)
  • Infrastructure: Docker, Terraform, Kamal (simplified deployment)

Build vs Buy Examples:

  • Buy: Auth, payments, email, hosting, user management
  • Build: Core differentiator only
  • SaaS boilerplates: Can reduce time-to-first-version for common app scaffolding

Next Steps After Architecture Setup

Once your tech stack is chosen:

  1. Start building - Use boilerplate or scratch-build
  2. Automate early - CI/CD, deployments, backups
  3. Document decisions - Why you chose X over Y
  4. Monitor tech debt - Track ratio, schedule cleanup

Related skills:

  • systemization-documentation-expert for SOPs and handoffs
  • customer-retention-optimizer for onboarding and lifecycle automation

Sources

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