How to Organize Your Claude Setup - Stop putting more rules. Tell Claude to audit its Data Debt

Most AI setups fail because of "Data Debt"—a pile of conflicting rules and legacy instructions that suffocate the model’s intelligence. This video breaks down the Search & Destroy Audit, a workflow for Claude Code and GitHub-linked environments. Learn how to have Claude audit its own business rules, identify logic clashes, and move from a cluttered "Rule Stack" to a single, high-signal Identity. Stop fighting the machine and start leading it with simplicity.

Hi Product Hunt 👋 We’re sharing a new perspective on AI orchestration: Addition by Subtraction. Most users think adding more rules makes an AI smarter, but our testing shows that "Data Debt" actually makes models second-guess themselves. We’ve developed a workflow to clean your AI engine and restore its edge. This video breaks down the full audit workflow: - Identifying Data Debt: How to spot when your legacy rules are "punching" your new skills and ruining your brand voice. - The Claude Code Setup: How we treat business logic like actual code using GitHub-linked Markdown files. - The Search & Destroy Prompt: A specific walkthrough of the one prompt that forces Claude to audit itself and flag dead weight. - The Anthropic Proof: Insights from the engineers at Anthropic on why less "scaffolding" leads to better AI performance. - The Zero-Rule Test: Our 3-step maintenance protocol to ensure your setup stays lean and your output stays expensive. The main takeaway: Complexity is usually a sign that you don't trust the tech. Simplicity is a sign of leadership. Your AI setup should be getting simpler over time, not more crowded. Curious to hear from the community: When was the last time you actually deleted a custom instruction? Are you building a clean engine, or just a pile of rules? Drop your thoughts below 👇

How to Organize Your Claude Setup - Stop putting more rules. Tell Claude to audit its Data Debt

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