What is adaptive thinking in Claude Opus 4.6?

Adaptive thinking allows the model to calibrate its reasoning depth across four effort levels (low, medium, high, and max), allowing for more efficient resource usage based on task complexity.

How does prompting for Opus 4.6 differ from earlier versions?

Opus 4.6 favors natural language over aggressive directives like 'MUST' or 'CRITICAL.' It also introduces adaptive thinking levels and removes support for assistant prefilling in favor of structured outputs.

How should I handle long contexts in Opus 4.6?

With the 1M token context window (beta), context rot is eliminated. This skill provides patterns to maintain quality in long conversations and use server-side compaction APIs.

What are agent teams in the context of this skill?

Agent teams represent a shift from sequential sub-agent delegation to parallel coordination, allowing multiple specialized agent instances to work on a project simultaneously for higher efficiency.

Opus 4.6 Prompt Engineering

Name: Opus 4.6 Prompt Engineering
Author: AeyeOps

byAeyeOps

0•

Data Science & ML

Optimizes and migrates prompts for Claude Opus 4.6's adaptive thinking, 1M context window, and advanced agentic architectures.

This skill provides specialized guidance for harnessing the power of Claude Opus 4.6, focusing on the transition from directive-heavy prompts to natural language instructions. It enables developers to implement adaptive thinking effort levels, manage massive 1M token context windows without quality degradation, and move from sequential sub-agents to parallel agent team architectures. By utilizing provided patterns, migration checklists, and analysis scripts, users can ensure their AI-assisted development workflows are fully optimized for the latest model capabilities, including server-side compaction and enhanced tool trigger sensitivity.

Key Features

01Parallel agent team architecture design patterns

020 GitHub stars

03Automated prompt analysis for deprecated patterns

04Migration tools for legacy 4.5 and earlier prompts

05Adaptive thinking calibration (Low to Max effort levels)

061M token context window optimization and management

Use Cases

01Optimizing long-context codebase analysis and RAG prompts

02Designing high-complexity autonomous agent workflows

03Upgrading legacy LLM prompts to Claude Opus 4.6 standards

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add aeyeops/aeo-skill-marketplace opus-prompting

For use in Claude.ai and ChatGPT

Download Skill