# Opus 5 更智能体化，提示词需精简

- 来源：elvis (@omarsar0)
- 发布时间：2026-07-30 03:34
- AIHOT 分数：66
- AIHOT 链接：https://aihot.virxact.com/items/cms6i9mmh0446rohzluchsdln
- 原文链接：https://x.com/omarsar0/status/2082550450373451938

## AI 摘要

Opus 5 被训练得比以往任何模型都更具智能体特性，喜欢自主探索，无需过多引导。用户应精简系统提示和 CLAUDE.MD，移除记忆和工具描述，采用清晰简洁的提示词与技能。Anthropic 已发布新的上下文工程指南，帮助用户适应这一变化。

## 正文

After a few more hours， I think I've figured out Opus 5.

Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that.

So what changes？

The way to interact with Opus 5 or contextualize it won't work the same way as with other models.

It loves exploring， so it doesn't need much guidance for it. Unique preferences， artifacts， and references compliment it well and enable cleaner and more effective exploration and execution.

Now that it can explore more effectively on its own and understand intent better， the best thing to do is to get out of its way （e.g.， it doesn't need examples of your preferences； a clear high-level description of it works best）. It's truly agentic in that sense.

A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence.

Regardless， persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it.

On the situational side， agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions， which are common at this layer （mainly to ensure reliability）， are going to throw off this model easily. That's the biggest change I had to make.

Simple， clean， and clear prompts and skills work best.

I had to clean a lot of my skills and system prompts. The way I prompt remains the same （usually clear and well-scoped）. MCP tool descriptions are also more descriptive and have been deduped from the system prompt.

Anthropic released a guide on the new rules for context engineering， which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way.

This might feel like a lot of work. Believe me， it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now.

@bcherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained， which is to be extremely agentic in nature and more direct in execution.