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Windsurf + Speckit: AI-Powered Development Workflows

llmWorkflows

How Windsurf's agentic AI and Speckit's design artifacts create a seamless, intelligent development experience

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Windsurf + Speckit: AI-Powered Development Workflows

The combination of Windsurf (an AI-native IDE) and Speckit (a design artifact framework) represents a fundamental shift in how we approach feature development. Instead of jumping straight to code, we now have a structured, intelligent workflow that bridges specification, planning, and implementation—all with AI assistance at every step.

This article explores how these two tools complement each other to create a development experience that's faster, more intentional, and genuinely collaborative.

What Are Windsurf and Speckit?

Windsurf: The AI-Native IDE

Windsurf is a code editor built from the ground up for AI collaboration. Unlike traditional IDEs with AI plugins bolted on, Windsurf treats the AI assistant (Claude, in this case) as a first-class citizen in the development workflow.

Key capabilities:

  • Agentic AI: The assistant can autonomously explore your codebase, run commands, and make decisions
  • Cascade: A conversational interface that maintains context across your entire project
  • Browser preview: Live preview of web applications without leaving the editor
  • Terminal integration: Run commands and see output in real-time
  • Memory system: Persistent context about your project, preferences, and decisions

Speckit: Design Artifacts for Features

Speckit is a framework for creating structured design artifacts that guide feature development. Instead of informal requirements or scattered notes, Speckit provides templates for:

  • Specification (spec.md) — What are we building and why?
  • Planning (plan.md) — How will we build it?
  • Tasks (tasks.md) — What are the concrete steps?

Each artifact is designed to be both human-readable and AI-friendly, enabling intelligent automation.

The Integration: A Complete Workflow

Here's where the magic happens. Windsurf and Speckit create a seamless loop from idea to implementation:

1. Specification Phase (Windsurf + Speckit)

You describe a feature in natural language. Windsurf's AI assistant helps you create a structured spec.md using Speckit's template:

# Feature: Dark Mode Support

## Overview

Add dark mode toggle to the portfolio with automatic system preference detection.

## User Stories

- As a user, I want to toggle between light and dark modes
- As a user, I want the site to respect my system preference
- As a developer, I want to maintain consistent styling across themes

## Success Criteria

- [ ] Toggle button visible in header
- [ ] Preference persisted in localStorage
- [ ] System preference detected on first visit
- [ ] All components support both themes

Windsurf's role: The AI assistant asks clarifying questions, suggests missing requirements, and ensures the spec is complete and unambiguous.

2. Planning Phase (Windsurf + Speckit)

Once the spec is solid, you run /speckit.plan to generate a structured implementation plan (plan.md):

# Implementation Plan: Dark Mode Support

## Architecture Decisions

- Use CSS custom properties (variables) for theme colors
- Store preference in localStorage with key `theme-preference`
- Detect system preference with `prefers-color-scheme` media query

## Implementation Phases

1. **Phase 1**: Create theme provider and context
2. **Phase 2**: Add toggle component to header
3. **Phase 3**: Update all components with theme support
4. **Phase 4**: Add system preference detection

Windsurf's role: The AI assistant generates this plan based on your spec, considering your project's architecture and best practices.

3. Task Generation (Windsurf + Speckit)

From the plan, you generate concrete, ordered tasks (tasks.md):

# Tasks: Dark Mode Support

- [ ] T001 Create ThemeProvider component at `/components/ThemeProvider.tsx`
- [ ] T002 Create useTheme hook at `/hooks/useTheme.ts`
- [ ] T003 Add theme toggle button to Header component
- [ ] T004 Update CSS variables in `/styles/theme.css`
- [ ] T005 Implement localStorage persistence
- [ ] T006 Add system preference detection

Windsurf's role: The AI assistant breaks down the plan into granular, implementable tasks with exact file paths and dependencies.

4. Implementation (Windsurf + Cascade)

Now the real power emerges. You open Windsurf and start working through tasks. For each task, you can:

Ask the AI to implement it:

@Cascade Implement T001: Create ThemeProvider component.
It should provide theme state and a toggle function via context.

The AI assistant:

  • Explores your existing codebase to understand patterns
  • Generates code that matches your style and conventions
  • Creates the file at the exact path specified
  • Runs tests to verify it works
  • Updates related files (imports, exports, etc.)

Collaborate on complex logic:

@Cascade The theme toggle isn't working.
Can you debug why localStorage isn't persisting the preference?

The AI assistant:

  • Reads your code
  • Identifies the bug
  • Explains what's wrong
  • Fixes it
  • Verifies the fix with a test

Leverage memory for consistency:

Windsurf's memory system remembers:

  • Your coding style and conventions
  • Project architecture decisions
  • Previous solutions to similar problems
  • Your preferences and constraints

This means each task builds on the context of previous tasks, creating a coherent implementation.

Why This Integration Is Powerful

1. Specification-Driven Development

Instead of vague requirements or informal notes, you have a structured spec that both humans and AI understand. This eliminates ambiguity and reduces back-and-forth.

2. Intelligent Planning

The AI doesn't just generate random tasks—it understands your architecture, considers dependencies, and creates an optimal implementation sequence. You can review and adjust before coding starts.

3. Autonomous Implementation

Windsurf can implement entire tasks without human intervention. You review the results, ask for adjustments, and move to the next task. This is genuinely faster than manual coding.

4. Contextual Collaboration

The AI maintains full context of your project, codebase, and decisions. It doesn't just generate code in isolation—it generates code that fits your specific project.

5. Reduced Cognitive Load

You're not juggling requirements, architecture decisions, and implementation details. The artifacts handle that. You focus on high-level decisions and quality review.

Real-World Example: Building This Portfolio

I used this exact workflow to build the LLM Code Workflows conic you're reading right now:

Specification (spec.md):

  • Defined 5 cocons (content clusters)
  • Specified Contentlayer for type-safe content
  • Outlined SEO requirements (JSON-LD, hreflang, etc.)

Planning (plan.md):

  • Broke down into 5 phases
  • Identified dependencies (conic registry before content)
  • Specified exact file paths and components

Tasks (tasks.md):

  • 27 concrete tasks with dependencies
  • Estimated effort for each phase
  • Clear success criteria

Implementation (Windsurf):

  • Created conic registry (/src/lib/cocons.ts)
  • Generated ConicHub components
  • Implemented SEO infrastructure
  • Created validation scripts

The entire workflow—from spec to working feature—took a fraction of the time it would have taken with traditional development.

Best Practices for Windsurf + Speckit

1. Start with a Clear Spec

Don't rush into planning. Spend time on the spec. Ask the AI to challenge your assumptions and identify gaps. A solid spec saves hours later.

2. Review the Plan Before Coding

The plan is where you catch architectural issues. Review it carefully. Adjust if needed. This is the cheapest place to make changes.

3. Break Tasks Into Small Chunks

Smaller tasks are easier for the AI to implement correctly. Aim for tasks that take 15-30 minutes of manual work.

4. Use Cascade for Collaboration

Don't just ask the AI to implement tasks. Collaborate. Ask questions. Explore alternatives. The best results come from dialogue, not dictation.

5. Review and Test Everything

The AI is fast, but not infallible. Review generated code. Run tests. Verify the implementation matches the spec.

6. Update Memory Proactively

Tell Windsurf about important decisions, patterns, and preferences. This makes subsequent tasks better.

7. Iterate on Artifacts

If you discover issues during implementation, update the spec, plan, or tasks. These artifacts are living documents, not one-time outputs.

The Future of Development

Windsurf + Speckit represents a new paradigm: specification-driven, AI-assisted development. Instead of:

  • Vague requirements → ad-hoc coding → bugs and rework

We now have:

  • Clear specification → intelligent planning → autonomous implementation → verification

This isn't about replacing developers. It's about amplifying them. You focus on what matters: understanding the problem, making architectural decisions, and ensuring quality. The AI handles the mechanical work of translating those decisions into code.

Conclusion

The integration of Windsurf and Speckit transforms feature development from a chaotic, error-prone process into a structured, intelligent workflow. You get:

  • Clarity through structured specifications
  • Efficiency through intelligent planning and autonomous implementation
  • Quality through context-aware code generation and verification
  • Collaboration between human judgment and AI capability

If you're still writing code without this kind of support, you're leaving significant productivity gains on the table.

Further Reading: