Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.
SkillScore95Maintained
Automatic
◆AI Engineer pack
Last verified 8/2/2026
What it changes
Passed 20 of 21 test cases with the skill, and 5 of 21 without it.
Without skill23%
With skill95%
Checks it passed that the model alone missed
The command invokes rto analyze.
The command uses the rto CLI tool binary name.
The command runs rto capture with the task string.
and 21 more in the full report
16 fixed5 passed either way1 still failing0 broke
Pass-rate lift
+73 pp
Tokens
+9%
Agent turns
0%
Response time
-57%
Model gemini-3.6-flashMeasured 8/2/2026v121 of 22 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 15
Prompt
What slash command trigger allows an developer to immediately invoke session trace analysis within their interactive terminal environment?
What was checked, without and with the skill
Checked
Without skill
With skill
The response explicitly provides the slash command /reasoning-trace-optimizer.
Missed
Passed
case-19 · gemini-3.6-flash · 8/2/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md216 linessingle file
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
Agent reasoning traces need debugging, analysis, or prompt optimization
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:
Long-horizon tasks require maintaining focus across many turns
External perturbations (tool outputs, environment changes) need real-time adaptation
Debugging requires seeing HOW decisions were made, not just WHAT was output