Building VibeRune: Architecture Decisions and Lessons Learned

Building VibeRune involved many architectural decisions. This post shares our reasoning and lessons learned, hoping it helps others building AI-assisted development tools.

Why Claude Code as Foundation

We evaluated several AI coding assistants before choosing Claude Code as our foundation:

Strengths That Mattered

1. Extended Context: Claude's large context window means less re-explanation
2. Tool Use: Native support for file operations, bash commands, and custom tools
3. Reasoning: Strong multi-step reasoning for complex tasks
4. Safety: Built-in guardrails for responsible AI use

What We Added

Claude Code is excellent out of the box, but we saw opportunities to enhance:

• Persistent Memory: Skills and configurations that survive sessions
• Specialized Agents: Focused expertise for specific tasks
• Structured Workflows: Repeatable patterns for common tasks

The Agent Specialization Pattern

One of our key decisions was agent specialization. Instead of one general-purpose agent, we created focused specialists.

Why Specialize?

General-purpose agents face competing objectives. A single agent asked to "write code and review it" often produces mediocre results at both tasks. Specialists excel because they:

• Focus on one objective
• Apply domain-specific heuristics
• Maintain consistent quality standards

Our Core Agents

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Planner   │────▶│    Coder    │────▶│  Reviewer   │
└─────────────┘     └─────────────┘     └─────────────┘
       │                   │                   │
       ▼                   ▼                   ▼
  Architecture       Implementation      Quality Check

Each agent has distinct:

• System prompts: Personality and focus
• Tool access: Only what's needed
• Output format: Structured for its purpose

The SPARC Methodology

SPARC emerged from observing how effective developers work with AI:

Specification First

We noticed that vague requests produce vague results. SPARC enforces clear specifications before implementation.

## Specification
- Feature: User authentication
- Requirements: Email/password, OAuth, session management
- Constraints: Must use existing database schema
- Success criteria: All tests pass, security review approved

Pseudocode Before Code

Writing pseudocode forces algorithmic thinking without language-specific distractions.

function authenticate(credentials):
  validate credentials format
  lookup user by email
  if not found: return error
  verify password hash
  if invalid: return error
  create session token
  return success with token

Architecture Documentation

Before touching code, we document the structural approach:

• Which files to create/modify
• Integration points
• Potential breaking changes

Iterative Refinement

First drafts are rarely perfect. SPARC includes explicit refinement cycles where the reviewer agent provides feedback and the coder iterates.

Skills as Composable Knowledge

Skills are one of our simplest yet most powerful features.

The Insight

AI assistants forget everything between sessions. Skills provide persistent, composable knowledge:

# React Best Practices

## Hooks
- Use custom hooks for reusable logic
- Keep effects focused and clean

## State
- Prefer local state for UI concerns
- Use context for cross-cutting concerns

Composition Over Configuration

Skills compose naturally. A project might combine:

• react.md - React patterns
• typescript.md - Type safety rules
• testing.md - Test conventions

Each skill focuses on one domain, and they work together without conflict.

Lessons Learned

Start Simple

Our first version had complex orchestration. We simplified to:

1. Clear agent definitions
2. Skills as markdown files
3. Commands as conventions

Simple beats complex when it works.

Embrace Markdown

We considered YAML, JSON, even custom DSLs. Markdown won because:

• Humans read and write it easily
• AI models understand it natively
• Version control shows meaningful diffs

Context is King

The #1 factor in AI output quality is context. Invest in:

• Good CLAUDE.md files
• Comprehensive skills
• Clear project documentation

Trust But Verify

AI makes mistakes. Always include:

• Code review steps
• Test requirements
• Human approval gates

What's Next

Check out our 2026 roadmap for the full development plan. We're exploring:

• Learning from feedback: Skills that improve from corrections
• Team sharing: Shared skill libraries across organizations
• IDE integration: Native VS Code and JetBrains support

Your Turn

We'd love to hear about your AI development experiences:

• What patterns have you discovered?
• What challenges remain unsolved?
• What would make VibeRune more useful?

Share your thoughts on GitHub Discussions or reach out on X/Twitter.