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reasoning-trace-optimizer

Capture MiniMax M2.1 interleaved thinking traces and analyze tool-by-tool behavior.

1|Updated May 16, 2026
Search Tags:#tool-calling#prompt-optimization#pattern-detection#agent-debugging#reasoning-traces#interleaved-thinking#agent-iteration
One-click install
npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill reasoning-trace-optimizer-enigmaicon-eng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: reasoning-trace-optimizer
Source: https://github.com/enigmaicon-eng/AI-Enterprise-OS/tree/main/examples/agent-skills/examples/interleaved-thinking
Command: npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill reasoning-trace-optimizer-enigmaicon-eng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires anthropic, pydantic, rich, python-dotenv.

What problem does it solve?

When AI agents fail, teams often only see the final output—missing the reasoning and tool-by-tool context that reveals where the behavior went wrong (e.g., tool confusion, context degradation, or instruction drift). This Skill helps you capture and analyze interleaved reasoning traces, then generate prompt improvements that address the detected failure patterns.

Core Features & Use Cases

  • Reasoning trace analysis: Detects recurring agent failure patterns using MiniMax M2.1 interleaved thinking.
  • Actionable prompt optimization: Produces concrete improvements aimed at eliminating specific issues (like context_degradation, tool_confusion, instruction_drift, missing_validation).
  • Iterative debug workflow: Supports a capture → analyze → optimize → re-run loop to converge on better performance.
  • Shareable skill generation: Converts debugging/optimization learnings into reusable Agent Skills for the team.

Quick Start

Activate the skill with your request to "debug agent" and include the agent task that failed; the skill will capture the interleaved reasoning trace, analyze patterns, and output a prompt improvement plan.

Frequently Asked Questions about reasoning-trace-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug AI agents with opaque reasoning and tool confusion failures?▼

Capture interleaved reasoning traces using MiniMax M2.1 to debug AI agents with opaque reasoning and tool confusion. The analysis detects failure patterns like context degradation and instruction drift to reveal where behavior went wrong.

How do I optimize prompts to fix AI agent instruction drift?▼

Fix AI agent instruction drift by analyzing captured reasoning traces to produce concrete prompt improvements. This applies JSON-pattern analysis with severity scoring to eliminate specific failure issues in long-horizon tasks.

What is the best way to analyze tool-by-tool behavior in failing multi-tool agents?▼

Analyze tool-by-tool behavior in failing multi-tool agents by capturing interleaved thinking traces for JSON-pattern analysis. This identifies goal abandonment and missing validation by scoring failure severity across the workflow.

Can I convert agent debugging learnings into reusable Agent Skills?▼

Yes, you can convert agent debugging learnings into reusable Agent Skills. The trace optimizer optionally generates shareable agent skills from optimization artifacts to preserve debugging improvements for your team.

Does this agent debugging workflow support iterative re-runs?▼

Yes, the agent debugging workflow supports iterative re-runs through a capture, analyze, optimize, and re-run loop. This iterative process converges on better performance by applying prompt improvements to subsequent agent tasks.

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