Anthropic says your prompt instructions are the workload

Anthropic's platform post, published September 8, names six prompt anti-patterns that tax every request. The list starts with verification rituals like "double-check your work" and thoroughness boosters like "be maximally thorough." It also names mandatory scratchpad steps, stale few-shot examples, contradictory rules, and dated settings like hand-tuned thinking budgets.

A reader on r/ClaudeAI tested the claim on their own rule files. They found 66 instances of "must" and 54 of "never" across 120 total rules. There was no way to tell which ones were load-bearing and which were leftover habit. Anthropic's own /claude-api prompt-audit tool found the answer for its test set. Cutting the dead weight lowered cost 14.6% and raised accuracy 5.3% on average, with a 58% cost drop on LegalBench and 73% on tau2-bench retail.

That 73% number is the one to sit with. It means most of the tokens in that prompt weren't doing anything except making the model rehearse instructions it already follows without being told.

I've written every one of these lines myself. "Think step by step before you answer" measurably helped when Sonnet 3.5 shipped. It went into a file, then the next file, then the one after that. Nobody deletes prompt text, because deleting feels like pulling a safety net right before the model does something dumb.

Sebastian Raschka's newsletter passed along the same advice from a colleague and the Claude Code lead. Archive old AGENTS.md and SKILL.md content outright. Newer models understand a task well enough that old descriptions box them into worse answers than they'd find alone. His exception is the one worth keeping: skill files that save a real rediscovery cost, like where the deploy script lives.

The move is an audit, not a rewrite. For every rule, ask if it encodes a fact the model can't know on its own, or just an attitude you wanted it to have. Keep the facts, cut the attitudes, then run your own eval both ways, since Anthropic's percentages come from their benchmarks, not yours.