reasoning-trace-optimizer

Debug AI agent reasoning failures by capturing reasoning traces with MiniMax M2.1 to identify patterns like context degradation and instruction drift. The analysis detects tool confusion and generates optimized prompts to improve agent performance.

Interleaved thinking is a MiniMax M2.1 mechanism used to capture the full AI agent reasoning process. It detects reasoning patterns by analyzing traces for context degradation, tool confusion, and instruction drift during complex workflows.

Analyze an AI agent reasoning trace by running the Skill against the last executed task. It automatically identifies tool confusion and instruction drift patterns, then suggests prompt improvements to resolve the errors.

Yes, the reasoning trace optimizer requires MiniMax M2.1 specifically for its interleaved thinking capabilities. This model is necessary to capture the reasoning process and accurately detect failure patterns.

Yes, you can convert optimization learnings into reusable Agent Skills. The Skill automatically transforms the generated prompt improvements and trace analysis results into shareable components for your team.

Analysis of AI agent reasoning traces is limited to workflows involving tool use, context management, and error handling. It depends on MiniMax M2.1 interleaved thinking and requires Python dependencies like anthropic and pydantic to function.