reasoning-trace-optimizer

npx skills add majiayu000/claude-skill-registry --skill interleaved-thinking-muratcankoylan-agent-skills-for-con-2 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Reasoning Trace Optimizer Debug and optimize AI agents by analyzing their reasoning traces. This skill uses MiniMax M2.1's interleaved thinking to provide deep insight into agent decision-making and generate concrete improvements. ## When to Activate - User asks to "debug agent", "analyze reasoning", or "optimize prompt" - Agent task fails and user wants to understand why - User mentions "context degradation", "tool confusion", or "instruction drift" - Request to improve agent performance or reduce errors - User wants to generate shareable learnings from debugging sessions - After repeated failures on similar tasks ## Core Concepts ### Interleaved Thinking Unlike standard reasoning models that think once at the start, interleaved thinking allows reasoning BETWEEN each tool interaction. This is critical because: 1. **Long-horizon tasks** require maintaining focus across many turns 2. **External perturbations** (tool outputs, environment changes) need real-time adaptation 3. **Debugging** requires seeing HOW decisions were made, not just WHAT was output ### The Optimization Loop ``` Execute Agent → Capture Traces → Analyze Patterns → Optimize Prompt → Re-run ↑____________| ``` Each

Capture reasoning trace
rto capture "Search for Python tutorials" -s "You are a helpful assistant."
Analyze a task
rto analyze "Debug this code" -o analysis.txt
Run optimization loop
rto optimize "Research AI papers" --max-iterations 5 --generate-skill
Generate skill from artifacts
rto generate-skill my-skill-name --artifacts-dir ./optimization_artifacts