Getting the most out of Opus 5.5 in Claude and Claude Code

You can now get longer, more complex coding tasks done in a single session with Claude Code without constant oversight.

The claude.dev blog has published a guide on maximizing the capabilities of Opus 5.5, the latest version of Anthropic's AI model, within Claude and Claude Code applications. The guide, authored by Addy Osmani, details how to effectively prompt the model, manage extended tasks, and verify outputs. Key strategies include providing the entire task upfront with a clear definition of 'done,' and eliminating redundant 'think carefully' instructions, as Opus 5.5 inherently processes information before responding. For long-running coding tasks in Claude Code, users can define specific stopping points in a CLAUDE.md file, such as before destructive actions or when human input is required. The guide also emphasizes the model's improved ability to handle multi-step work and its enhanced accuracy in interpreting visual data like charts and screenshots in Claude apps. Users are encouraged to attach images directly rather than retyping data and to request final file formats for generated content like spreadsheets.

Opus 5.5's enhanced multi-step task execution and improved context retention mean users can delegate more complex, extended projects to the AI. By clearly defining the 'done' state and stopping conditions within prompts or configuration files like CLAUDE.md, developers can reduce the need for manual intervention and context-switching. This capability extends to specialized agents within Claude Code, allowing for parallel processing and more robust code audits and migrations with significantly less developer oversight.

The integration of Opus 5.5's advanced visual interpretation in Claude apps streamlines workflows by enabling direct analysis of charts, diagrams, and screenshots. Users no longer need to manually transcribe data, which reduces errors and accelerates tasks requiring visual data processing. Furthermore, the model's improved output consistency and clarity for generated documents and spreadsheets mean less post-generation editing, allowing for quicker deployment and integration of AI-assisted content.