What is interleaved thinking in agent systems?

Interleaved thinking allows the model to reason and re-evaluate its strategy between each tool interaction, which is critical for maintaining focus during long-horizon tasks.

How do I integrate this with my Claude Code workflow?

You can use the built-in slash command or configure post-tool-error hooks to automatically trigger a reasoning analysis when a failure occurs.

What are the common failure patterns detected?

The analyzer identifies issues like context degradation, tool confusion, instruction drift, goal abandonment, circular reasoning, and premature conclusion.

How does the optimization loop improve agent prompts?

It executes the agent, captures reasoning traces, detects failure patterns, and applies a feedback loop to refine the system prompt until the performance score improves.

Does this skill only work with specific LLMs?

While optimized for MiniMax M2.1's unique thinking capabilities, it features a Universal Session Analysis mode that works with Claude, GPT, and other major models.

Reasoning Trace Optimizer

Name: Reasoning Trace Optimizer
Author: muratcankoylan

bymuratcankoylan

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14,414

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Analytics & Monitoring

Debugs and optimizes AI agents by analyzing interleaved reasoning traces and identifying failure patterns.

The Reasoning Trace Optimizer is a specialized tool designed to improve AI agent performance by capturing and analyzing internal reasoning steps. By leveraging MiniMax M2.1's interleaved thinking capabilities, it identifies common failure modes such as context degradation, tool confusion, and instruction drift. This skill enables developers to automate the prompt optimization loop, turning debugging sessions into actionable, shareable learnings and high-quality system prompts for production-ready multi-agent systems.

Key Features

01Live Session Analysis: Directly diagnoses Claude Code session failures in real-time.

02Failure Pattern Detection: Identifies context degradation, tool confusion, and instruction drift.

03Interleaved Thinking Analysis: Evaluates reasoning between tool calls for deep logic debugging.

04Automated Optimization Loop: Iteratively improves prompts based on captured performance metrics.

05Skill Generation: Converts successful optimization artifacts into reusable agent skills.

0614,414 GitHub stars

Use Cases

01Diagnosing complex multi-step task failures in autonomous agents.

02Extracting best practices from debugging logs into modular agent skills.

03Automating prompt engineering to reduce tool selection errors.

Key Features

01Live Session Analysis: Directly diagnoses Claude Code session failures in real-time.

02Failure Pattern Detection: Identifies context degradation, tool confusion, and instruction drift.

03Interleaved Thinking Analysis: Evaluates reasoning between tool calls for deep logic debugging.

04Automated Optimization Loop: Iteratively improves prompts based on captured performance metrics.

05Skill Generation: Converts successful optimization artifacts into reusable agent skills.

0614,414 GitHub stars

Use Cases

01Diagnosing complex multi-step task failures in autonomous agents.

02Extracting best practices from debugging logs into modular agent skills.

03Automating prompt engineering to reduce tool selection errors.