Izzo: You know that moment when you're debugging why your AI agent keeps doing exactly what you told it not to do?
Izzo: Welcome back to Exploring Next, episode 260. I'm Izzo, and with me as always is Boone. Today we're diving into something that just dropped on Reddit — the leaked Claude Code prompts and what they reveal about building agents that actually work.
Boone: And Izzo, this isn't just academic. Someone reverse-engineered every prompt from Claude Code's source when it was briefly public on npm, then rewrote them from scratch using Claude itself.
Izzo: Right, so we're looking at battle-tested patterns from Anthropic, not just theory. And the timing couldn't be better — everyone's building agents right now, but most are failing because of terrible prompt engineering.
Boone: Exactly. The person who did this analysis found patterns that work regardless of which model you're using. First big one: explicit anti-patterns.
Izzo: Break that down for me, Boone.
Boone: Most GPT agents just tell the model what to do. Claude Code spends equal time saying what NOT to do. Like, instead of 'use the shell command,' they say 'don't use shell for file operations, don't use shell for text processing.'
Izzo: That's fascinating because from a product perspective, negative constraints are how you actually control behavior. It's like UX — you guide users by removing bad paths, not just highlighting good ones.
Boone: Exactly! And they take this further with risk tiers instead of blanket safety rules. Three categories: reversible actions you can do freely, hard-to-reverse that need confirmation, and anything visible to others that always requires permission.
Izzo: Okay, that's way more sophisticated than the usual 'always ask before doing anything' approach that makes agents useless.
Boone: Right. But here's where it gets really interesting — they have a separate verification agent. A dedicated agent whose job is to try breaking the implementation.
Izzo: Wait, like a red team agent?
Boone: Basically, yeah. It watches for six specific rationalizations that indicate the main agent is about to do something stupid. This pattern works with any model, not just Claude.
Izzo: I'm giving this approach an A-minus already. What about memory? That's where most agents fall apart after a few turns.
Boone: They don't just summarize conversations. They use a structured 9-section format that preserves user messages, code snippets, errors, and next steps separately.
Izzo: So instead of losing context in a summary, you're maintaining the actual structure of what happened.
Boone: Exactly. And for tool routing, they lean heavy on those negative rules we mentioned. 'Don't use shell for X' is way more reliable than 'you can use shell for Y.'
Izzo: From a market angle, this explains why Claude Code felt so much more reliable than other coding agents. They're not just throwing prompts at a wall.
Boone: The legal side is interesting too. The person who analyzed this used Claude to rewrite everything in original words, ran automated originality checks to confirm zero verbatim matches.
Izzo: Smart. So we're looking at the patterns, not copying the actual prompts.
Boone: Right. And honestly, these patterns solve problems I see in every agent I've tried to build. The verification agent alone would've saved me so many weekends.
Izzo: Speaking of weekends, let me guess — you're adding this to the project list?
Boone: Already there. But seriously, the structured memory format is something anyone can implement today.
Izzo: So if you're building agents or just trying to get better results from AI, here's what to go research. First, check out the GitHub repo — github.com/swati510/claude-code-prompts.
Boone: Second, pick one agent you're already using and add explicit negative rules. Just start with 'don't do X' alongside your existing 'do Y' instructions.
Izzo: And third, build a simple verification agent for whatever domain you're working in. Even a basic one that checks for common failure modes will catch issues the main agent misses.
Boone: The beauty is these patterns stack. Start with negative rules, add risk tiers when you need more control, then layer in verification as you scale up.
Izzo: Next time you're wondering why your agent keeps ignoring your instructions, remember — sometimes the best way to get what you want is being really clear about what you don't want.