Boris Cherny built Claude Code. So when he says his own team deletes most of its accumulated prompt scaffolding every time Anthropic ships a new model — and the product gets better, not worse — it’s worth paying attention.

In a recent talk that’s been circulating widely across X in clip form (reportedly from a Y Combinator Startup School session), Cherny described a habit that runs directly against the instinct most teams have when working with AI coding agents: the instinct to keep adding rules. His advice is the opposite. Delete your CLAUDE.md, your skills, and your hooks roughly every six months. Watch what the model does unguided. Only add rules back after it fails at the same thing twice.

This is a strong claim, and it’s important to be upfront about its sourcing before we get into the mechanics: this comes from an interview and talk circuit, not an official Anthropic blog post or engineering document. Multiple independent write-ups — including finance and tech outlets and at least one dedicated video breakdown — corroborate the specific “80% deleted” figure tied to the Opus 5 generation, so it’s not an isolated rumor. But treat it as practitioner testimony from the person who built the tool, not as an official Anthropic policy statement.

The Core Claim

According to Cherny’s account, with each new Claude model release, his team goes back through Claude Code’s system-prompt scaffolding — the accumulated instructions, corrections, and guardrails built up over time — and deletes most of it. For the transition tied to Opus 5, he says roughly 80% of the existing rules were cut. Rather than degrading, performance improved.

His explanation is straightforward: most of that scaffolding wasn’t foundational guidance. It was “scar tissue” — patches written to correct specific weaknesses in older, weaker models. Rules like “don’t add comments unless asked,” “always read the file before editing it,” or elaborate multi-step recipes for tasks the model used to get wrong. As models get more capable, those patches stop being helpful guidance and start being unnecessary constraints — bloat that narrows what a smarter model would otherwise do well on its own.

The broader context Cherny gives, per the clips and summaries circulating: newer models like Opus 5 can reportedly sustain long-running, largely unsupervised work — sessions cited in the tens of days, with one example of an 11-day run producing over 100,000 lines of code, and another describing a two-week autonomous rewrite. The argument is that heavy prompt scaffolding, tuned for models that needed constant correction, actively gets in the way of models that don’t need it anymore.

The Ritual, Step by Step

Based on the lessons attributed to Cherny across the circulating clips and summaries, here’s the practical version of the exercise:

1. Pick your trigger point. Cherny’s suggested cadence is roughly every six months, or whenever you upgrade to a meaningfully newer model generation — whichever comes first. Don’t wait for a crisis; treat it as scheduled maintenance, the same way you’d schedule a dependency audit.

2. Back up before you delete anything. Before removing anything, commit your current CLAUDE.md, skill definitions, and hook configurations to version control (or copy them somewhere safe) so you have a clean rollback path. This isn’t optional — you’re about to intentionally remove guardrails, and you want an easy way back if something goes sideways.

3. Delete your accumulated scaffolding. Remove the bulk of your CLAUDE.md content, project-level skills, and hooks. The instinct here is to be aggressive rather than cautious — the entire premise of the exercise is that most of what’s accumulated is now dead weight rather than load-bearing guidance.

4. Run the model unguided on real work. Give it representative tasks from your actual workflow — not toy examples — and watch closely. Don’t intervene preemptively. The point is to observe what the model does with its own judgment before you decide it needs correcting.

5. Only re-add a rule after a repeated, specific failure. The stated bar in Cherny’s framing is that a rule earns its way back only after the model fails at the same thing twice — not on a single mistake, and not preemptively because you’re worried it might fail. This keeps the rule set lean and evidence-based rather than defensively bloated.

6. Keep what’s genuinely load-bearing. Not everything should go. Hard facts about your codebase — deployment commands, internal library locations, test file locations, project-specific goals and exit criteria, and verification steps — are context, not scar tissue. The distinction Cherny draws is between brittle step-by-step recipes (delete these) and durable facts and verification criteria (keep these).

Why This Matters Beyond Claude Code

The broader lesson generalizes past any single tool: as agentic coding models improve, the return on investment for heavy prompt engineering may be shifting. Cherny’s framing, echoed in the wider discussion around this talk, is that the industry’s center of gravity is moving away from writing longer and longer instruction files and toward building strong verification systems — adversarial code review, cross-file checks, test harnesses that catch what the model gets wrong, rather than prose that tries to prevent it from getting things wrong in the first place.

If your own CLAUDE.md, agent skill files, or hook configurations have been accumulating edits for the better part of a year without a serious pruning pass, this is a reasonable moment to ask whether you’re maintaining scaffolding your current model generation actually still needs — or whether you’re carrying forward workarounds for a version of the model that no longer exists.

A Note on Accuracy

This piece intentionally does not reproduce specific CLI commands, config file syntax, or product-specific settings tied to this pruning exercise, because those details were not confirmed against Anthropic’s official documentation for this article. The CLAUDE.md, skills, and hooks mechanisms themselves are real, documented parts of Claude Code — but the specific ritual described here is a practitioner recommendation from Cherny’s talk, not an Anthropic-published procedure. If you want to implement this, start by manually backing up and clearing your own project’s CLAUDE.md and skill/hook directories using your normal file operations, and refer to Claude Code’s official documentation for the exact settings-file syntax relevant to your version.

Sources

  1. GeniusThinking (@GeniusGTX) on X: Boris Cherny’s 16 lessons thread
  2. Y Combinator (@ycombinator) on X: Startup School 2026 session clip
  3. Claude Code documentation — Choose a permission mode (background on CLAUDE.md and settings files)

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