I’ve been using Claude Code since the early beta, and while the "99% of my code is AI-written" claims are technically true for boilerplate, I kept hitting a wall with complex backend refactors. The agent would get stuck in a "hallucination loop"—fixing a bug, breaking a route, then suggesting the same fix again.

I finally hit a 10x velocity boost by moving away from "chatting" and implementing a Deterministic Truth Layer.

The Problem: The Vibe Coding Ceiling "Vibe coding" is great until you hit a 50-80% probabilistic success rate. If the agent doesn't have a way to verify its own logic against a real server state, it’s just guessing.

The Solution: Apidog CLI as a Claude Skill Instead of letting Claude "think" it fixed the API, I linked my terminal to the Apidog CLI guide via a custom SKILL.md.

The 10x Workflow:

The Skill: I defined my API test suite as a core Claude Skill.

The Execution: When I tell Claude to "Refactor the Auth logic," it doesn't just write code. It triggers an Automated API Testing Guide scenario through the CLI.

The Self-Correction: Claude captures the CLI output (actual JSON responses, status codes). If a test fails, the agent performs a root-cause analysis on the logs and auto-corrects before I ever see the PR.

This moves the agent from "Fuzzy Reasoning" to "Deterministic Execution." It’s the only way I’ve found to actually reach 100% autonomous code generation for production-grade APIs.

I’ve put together a quick Claude Code Tutorial on how to map these CLI skills in the comments/link below. Curious if anyone else is using MCP or custom skills to provide a "ground truth" to their terminal agents?