THIS DUDE REALLY JUST LEAKED CLAUDE OPUS 4.7 SYSTEM PROMPT!! (link below)
PROMPT ENGINEERING
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Codex learns from experience with new browser and plugins
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Codex can learn from experience and proactively suggest things it can do for you. It now has an in-app browser, many new plugins, and so much more.
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Configure Opus 4.7 Effort Level for Adaptive Thinking
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5/ Configure your effort level
— Boris Cherny (@bcherny) 16 avril 2026
Opus 4.7 uses adaptive thinking instead of thinking budgets. To tune the model to think more/less, we recommend tuning effort.
Use lower effort for faster responses and lower token usage. Use higher effort for the most intelligence and capability.… pic.twitter.com/h2gY3yQEeK5/ Configure your effort level Opus 4.7 uses adaptive thinking instead of thinking budgets. To tune the model to think more/less, we recommend tuning effort. Use lower effort for faster responses and lower token usage. Use higher effort for the most intelligence and capability.
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Verify Claude’s Work to Maximize AI Output
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6/ Give Claude a way to verify its work Finally, make sure Claude has a way to verify its work. This has always been a way to 2-3x what you get out of Claude, and with 4.7 it's more important than ever. Verification looks different depending on the task. For backend work, make
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Claude Opus 4.7 Enhancement Enables Longer Agentic AI Workflows
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Happy coding! Opus 4.7 is a significant step up. To get the most out of it, take the time to adjust your workflow to take advantage of Claude running for longer & being more agentic. It feels like a nice improvement with old workflows, and a significant leap once you take the
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Maximizing Productivity with Opus 4.7: Practical Tips
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Dogfooding Opus 4.7 the last few weeks, I've been feeling incredibly productive. Sharing a few tips to get more out of 4.7
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Opus 4.7 Auto Mode Enables Autonomous Complex Task Execution
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1/ Auto mode = no more permission prompts Opus 4.7 loves doing complex, long-running tasks like deep research, refactoring code, building complex features, iterating until it hits a performance benchmark. In the past, you either had to babysit the model while it did these sorts
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Expanding Codex Use Cases Library for AI Applications
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We’re continuing to expand our library of use cases here to inspire people with what they can do with Codex. You can ask Codex almost anything!
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Developer Adoption Lag: How AI Models Drive Task Complexity Shifts
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The shift to harder tasks wasn't immediate. Developers first used better models to do more work of similar complexity. Only after a 4-6 week lag did they start tackling more ambitious problems.
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Opus 4.7 Adaptive Thinking Uses More Tokens Than 4.6
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Not accurate. Adaptive thinking lets the model decide when to think, which performs better. Opus 4.7 also uses more thinking tokens on average than 4.6, which is why we have increased rate limits for all subscribers to make up for it.