How to build your own AI developer tools with Claude Code | CJ Hess (Tenex)

What It Covers

CJ Hess demonstrates his custom AI development workflow using Claude Code, including Flowy, a self-built tool that converts JSON specifications into visual flowcharts and UI mockups. He shows how he uses model-to-model comparison with GPT Codex to review Claude's code, creates custom skills for automation, and bypasses permissions to accelerate development cycles.

Key Questions Answered

• •Custom Visual Planning Tool: Flowy transforms JSON files into visual flowcharts and UI mockups, replacing hard-to-read ASCII diagrams in markdown plans. Claude Code uses custom skills to write JSON specifications that render as interactive diagrams on localhost, allowing developers to iterate visually before writing code. The tool was built almost entirely through prompting and serves as living documentation.
• •Iterative Skill Development: Skills improve through usage rather than upfront design. When Flowy generates incorrect output like white text on pastel backgrounds, the workflow involves updating the skill file with new rules about spacing, colors, or layout. Each feature addition to Flowy includes updating documentation and related skills, creating a self-improving system that gets better with each project.
• •Model-to-Model Code Review: Using GPT Codex to review Claude-generated code catches different types of issues than human review. Codex excels at identifying code smells, suggesting refactoring approaches, and finding discrepancies between specifications and implementation. The workflow involves checking git diffs against four criteria: plan accuracy, code smells, alternative approaches, and opportunities to consolidate duplicate code patterns.
• •Permission Bypass for Solo Work: Terminal aliases like "Kevin" route to Claude Code with full bypass permissions enabled, eliminating approval friction during solo development. This approach works when Git workflows and team rules provide safety nets for dangerous operations. For collaborative work or PR creation, permission checks remain active, but individual feature development runs unrestricted to maximize velocity.
• •Code-as-Specification Pattern: Generate throwaway code to define requirements, then prompt the model to write a clean implementation plan based on that reference. This approach treats initial code generation as a specification document rather than production code. When vibe coding creates monster diffs with unclear structure, rebuilding from scratch with proper planning produces more maintainable, extensible results than iterative cleanup.