Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.
中文简介正在准备中。当前显示英语简介。
Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.
安装前可以查看交给 AI 代理的指令内容。
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.
Unlike standard reasoning models that think once at the start, interleaved thinking allows reasoning BETWEEN each tool interaction. This is critical because:
Execute Agent → Capture Traces → Analyze Patterns → Optimize Prompt → Re-run
↑____________|
Each iteration improves the prompt based on detected patterns until convergence.
Common failure patterns the analyzer detects:
| Pattern | Description |
|---|---|
context_degradation | Model loses track of information over long contexts |
tool_confusion | Model misunderstands tool capabilities or outputs |
instruction_drift | Model gradually deviates from original instructions |
goal_abandonment | Model stops pursuing the original goal |
circular_reasoning | Model repeats similar actions without progress |
premature_conclusion | Model concludes before completing the task |
Run a task through M2.1 and analyze its reasoning:
from reasoning_trace_optimizer import TraceCapture, TraceAnalyzer
capture = TraceCapture()
trace = capture.run(
task="Search for Python tutorials and summarize them",
system_prompt="You are a research assistant.",
tools=[search_tool],
tool_executor=execute_search
)
analyzer = TraceAnalyzer()
analysis = analyzer.analyze(trace)
print(f"Score: {analysis.overall_score}/100")
for pattern in analysis.patterns:
print(f"Found: {pattern.type.value} - {pattern.suggestion}")
Automatically iterate until the prompt is optimized:
from reasoning_trace_optimizer import OptimizationLoop, LoopConfig
config = LoopConfig(
max_iterations=5,
min_score_threshold=80.0,
)
loop = OptimizationLoop(config=config)
result = loop.run(
task="Analyze this codebase and suggest improvements",
initial_prompt="You are a code reviewer.",
tools=[read_file_tool, search_tool],
tool_executor=execute_tool
)
print(f"Improved: {result.initial_score} → {result.final_score}")
print(f"Final prompt:\n{result.final_prompt}")
Analyze any agent's previous thinking (works with Claude, GPT, etc.):
When this skill is activated in Claude Code, it can analyze the current session's thinking blocks to identify issues and suggest improvements.
/reasoning-trace-optimizer analyze-session
Convert optimization learnings into reusable Agent Skills:
from reasoning_trace_optimizer import SkillGenerator
generator = SkillGenerator()
skill_path = generator.generate(
result=loop_result,
skill_name="web-search-best-practices",
output_dir="./skills"
)
# 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
Add to your hooks to automatically analyze failures:
{
"hooks": {
"post_tool_error": {
"command": "rto analyze-session --last-error"
}
}
}
Use the slash command to analyze current session:
/reasoning-trace-optimizer
This will:
System: You are a helpful assistant.
Issue: Agent called wrong tools, lost track of goal after 3 turns
Score: 45/100
Patterns: tool_confusion, goal_abandonment
System: You are a research assistant focused on finding accurate information.
IMPORTANT GUIDELINES:
- Always verify search results before summarizing
- If a tool returns an error, try an alternative approach
- Keep track of your original goal throughout the task
- Validate findings against multiple sources when possible
Issue: None
Score: 85/100
Patterns: None detected
docs/interleavedthinking.mddocs/agentthinking.mdCreated: 2025-01-11 Author: Muratcan Koylan Version: 0.1.0 Powered by: MiniMax M2.1 Partnership: Built in collaboration with MiniMax AI
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Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.
中文简介正在准备中。当前显示英语简介。
Model what an agent believes, wants, and commits to doing. Connect external facts to goals and plans using structured relationships, with evidence and validity periods that help trace why the agent chose an action.
中文简介正在准备中。当前显示英语简介。
Turn ePub books into training examples for models that reproduce an author's writing style. Covers text extraction, coherent segmentation, varied instructions, model training, and checks for copied phrases and style transfer to new scenarios.
中文简介正在准备中。当前显示英语简介。
Guide multi-source research through source checks, comparison of key claims, and recovery from failed tool calls. Produce reports that distinguish retrieved evidence from unresolved questions and clearly document remaining gaps.
中文简介正在准备中。当前显示英语简介。
Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.
中文简介正在准备中。当前显示英语简介。
Diagnose why an AI agent becomes less reliable as conversations and documents accumulate. Distinguish missed information, persistent false claims, irrelevant material, mixed tasks, and conflicting sources, then choose ways to clean, reorganize, or separate the context.
中文简介正在准备中。当前显示英语简介。