Superpowers: Why Spec-Driven Development Changes Everything with AI Agents

January 12, 2026

I used to dive straight into code. Got an idea for a personal app? Start building. Feature request? Just implement it. I’d convinced myself that thinking was slower than doing, that real developers ship fast and iterate. Then I spent three days debugging an architecture that should have taken an afternoon because I never stopped to clarify what I was actually building.

The problem wasn’t the AI assistant helping me code. The problem was me. I was handing vague intentions to an AI and expecting it to read my mind about edge cases, user flows, and technical constraints I hadn’t articulated. The AI would confidently build exactly what I asked for, which wasn’t what I actually needed. That’s when I discovered Superpowers, and it fundamentally changed how I approach AI-assisted development.

The Chaos of “Just Start Building”

Before I found Superpowers, my workflow with AI coding agents was essentially chaos with good intentions. I’d describe a feature in conversational terms, watch the AI generate code, realize halfway through that I hadn’t considered authentication, backtrack, add authentication, discover the data model didn’t account for a key relationship, refactor everything, and eventually ship something that worked but felt held together with duct tape.

The AI wasn’t the problem. I was giving it impossible tasks: implement something I hadn’t fully designed, test against requirements I hadn’t specified, and integrate with systems I hadn’t documented. Every “quick fix” accumulated into a codebase that fought back against every change.

The worst part? I knew better. I’d worked on professional teams with proper design docs and PRDs. But for personal projects, I thought I could skip the ceremony. I was wrong.

Discovering Superpowers

I found the Superpowers framework while looking for better ways to structure AI-assisted development. Created by Jesse (obra), it’s a software development framework specifically designed for AI coding agents, and it’s built on a philosophy I’d been ignoring: systematic processes beat ad-hoc decisions every time.

What caught my attention was the mandatory design phase. Superpowers doesn’t let you skip straight to implementation. The AI agent asks clarifying questions, explores alternatives, and presents designs for your approval before writing a single line of code. This is exactly the discipline I’d been missing.

The framework has over 17,000 stars on GitHub, which told me I wasn’t alone in needing this structure. But the star count wasn’t what sold me. It was seeing how the phases mapped to every professional development workflow I’d experienced, just optimized for AI agents.

How Superpowers Structures Development

Superpowers breaks AI-assisted development into seven sequential phases. Each phase has specific goals and gates that prevent progress until the previous phase is properly complete.

flowchart TB subgraph Phase1["1. Design Phase"] D1[Clarifying Questions] D2[Explore Alternatives] D3[Present Design] D4[User Approval] D1 --> D2 --> D3 --> D4 end subgraph Phase2["2. Environment Setup"] E1[Create Isolated Branch] E2[Verify Clean Test Baseline] E1 --> E2 end subgraph Phase3["3. Planning"] P1[Break Into 2-5 Min Tasks] P2[Exact Specifications] P1 --> P2 end subgraph Phase4["4. Execution"] X1[Autonomous Agent Dispatch] X2[Spec Compliance Review] X3[Code Quality Review] X1 --> X2 --> X3 end subgraph Phase5["5. Testing"] T1[Write Tests First - TDD] T2[Reject Code Before Tests] T1 --> T2 end subgraph Phase6["6. Review"] R1[Validate Against Plan] R2[Block on Critical Issues] R1 --> R2 end subgraph Phase7["7. Completion"] C1[Verify All Tests Pass] C2[Present Merge/PR Options] C1 --> C2 end Phase1 --> Phase2 --> Phase3 --> Phase4 --> Phase5 --> Phase6 --> Phase7

Phase 1: Design

This is where everything changed for me. The agent doesn’t accept vague requirements. It asks clarifying questions, pushes back on ambiguity, and explores design alternatives before committing to an approach. The design phase forces you to articulate what you actually want before implementation begins.

Phase 2: Environment Setup

Superpowers creates isolated development branches and verifies a clean test baseline. No more accidentally breaking things in main while experimenting. The framework treats git worktree management as a first-class concern.

Phase 3: Planning

Work gets broken into 2-5 minute tasks with exact specifications. This granularity is intentional. Small tasks are easier to verify, easier to test, and easier to roll back if something goes wrong. Each task has clear success criteria.

Phase 4: Execution

Agents execute tasks autonomously, but every piece of work goes through a two-stage review: first spec compliance (did it build what was specified?), then code quality (is it built well?). This separation prevents scope creep and catches quality issues early.

Phase 5: Testing

This phase enforces Test-Driven Development as mandatory practice. The framework will reject code written before its tests. This isn’t optional. If you try to skip tests, the workflow blocks your progress. For someone like me who often skipped tests on personal projects, this forced discipline was initially annoying and ultimately invaluable.

Phase 6: Review

Every implementation gets validated against the original plan. Critical issues block progress. This prevents the gradual drift that happens when you “just fix this one thing” repeatedly until the implementation no longer matches the design.

Phase 7: Completion

The final phase verifies all tests pass and presents merge or PR options. Clean endings for clean code.

The Spec-First Mindset

What I appreciate most about Superpowers is how it made spec-first development feel natural. When I started using it for personal apps, I found myself actually enjoying the design phase. Writing a proper PRD before implementation wasn’t ceremony or overhead. It was thinking time that paid dividends during implementation.

Here’s a mental model that helped me internalize this:

flowchart LR subgraph Traditional["Traditional Approach ⟳ Chaos Loop"] direction TB T1[Vague Idea] --> T2[Start Coding] T2 --> T3[Hit Problem] T3 --> T4[Redesign] T4 --> T5[Refactor] T5 --> T3 end subgraph Superpowers["Superpowers Approach → Linear Progress"] direction TB S1[Vague Idea] --> S2[Design Phase] S2 --> S3[Clear Spec] S3 --> S4[Plan Tasks] S4 --> S5[Implement] S5 --> S6[Verify] end

The traditional approach creates a chaos loop where problems discovered during implementation force redesigns that create more problems. The Superpowers approach invests time upfront in design and specification, resulting in linear progress through implementation.

Philosophy That Resonates

Superpowers is built on four core principles that I’ve come to appreciate:

Test-Driven Development as mandatory practice. Not suggested. Mandatory. The framework won’t let you proceed without tests. This forced me to think about testability during design, which improved my designs.

Systematic processes over ad-hoc decisions. Every phase has clear gates and criteria. There’s no “I’ll fix it later” because the workflow won’t let you move forward with broken foundations.

Simplicity reduction as primary goal. Each phase aims to reduce complexity. The design phase simplifies requirements. The planning phase simplifies implementation. The review phase simplifies integration.

Evidence-based verification rather than assumptions. Tests provide evidence. Reviews validate against plans. Nothing proceeds on “it should work” or “this looks right.”

Platform Integration

Superpowers supports multiple AI coding platforms:

Claude Code: Most integrated experience, available via the plugin marketplace
Codex: Full framework support
OpenCode: Compatible with the workflow structure

I primarily use it with Claude Code, where the integration feels native. The framework’s skills and instructions compose naturally with Claude’s capabilities.

My Experience Building Personal Apps

Since adopting Superpowers, I’ve used it to build several personal apps. The difference is striking. Projects that would have taken me a weekend of frustrated debugging now flow smoothly from design through deployment.

One project involved building a personal finance tracker. In the past, I would have started by creating database tables and hoped the UI requirements would become clear. With Superpowers, the design phase forced me to articulate user flows, data relationships, and edge cases before any implementation. When I finally started coding, the AI agent had a clear specification to work against. Testing was straightforward because the requirements were explicit. The final result was cleaner and more maintainable than anything I’d built with the “just start coding” approach.

The framework’s granular task breakdown (2-5 minutes per task) was particularly valuable. Each task was small enough to verify completely before moving on. I caught issues immediately instead of discovering them three features later when they’d become deeply embedded.

Key Learnings

Spec-first development isn’t overhead: The time invested in the design phase pays back exponentially during implementation and maintenance
Mandatory TDD changes how you think: When you know tests come first, you design for testability from the start
Small tasks enable verification: Breaking work into 2-5 minute chunks makes it possible to catch issues before they compound
Gates prevent drift: Blocking progress on unmet criteria keeps implementations aligned with designs
AI agents need structure too: Giving an AI a clear spec produces dramatically better results than giving it vague intentions
Evidence beats assumptions: The framework’s insistence on verification at every stage eliminates the “it should work” problem
Personal projects deserve professional process: Skipping ceremony on personal apps led to frustration; applying structure led to satisfaction
Git isolation matters: Working in isolated branches with clean test baselines prevents the fear of breaking things while experimenting

Superpowers transformed my relationship with AI-assisted development. I stopped treating AI agents as magic code generators and started treating them as capable team members who need clear requirements, structured workflows, and systematic verification. The results speak for themselves: cleaner code, faster development, and projects I’m actually proud to maintain.

If you’ve been frustrated by unpredictable AI coding results, consider whether the problem is the AI or the process you’re giving it. Superpowers taught me that the process matters as much as the tools.