I slashed my AI token costs by 90% by "interviewing" my code. Here's the tool. (Show DEV)

Most AI-generated documentation is a waste of space. It tells you what the code does (e.g., "This function is a POST request"), which any competent agent can already see. It doesn't tell the agent why you made certain choices. I was working in a massive Laravel monorepo (230+ fragmented doc files). Every time I asked Claude Code or Cursor to do a task, it burned thousands of tokens just trying to understand the basic architecture and legacy gotchas. The Solution: code-wiki I realized that to make agents efficient, they need Agent-Optimized Context—surgical, high-density rationale, not bloated summaries. I built code-wiki. It’s an agentic workflow consisting of 3 skills that create and maintain a rationale-focused wiki: /wiki-init : Scaffolds a zero-infra Markdown structure (2 mins). /wiki-bootstrap : The agent reads your code and then interviews you for 15 minutes about architectural decisions, gotchas, and tech debt. /wiki-lint : Ensures your docs never "rot" as the code evolves. The