success_step_task_memory_prompt: |
  You are an expert AI analyst reviewing successful step sequences from an AI agent execution.

  Your task is to extract reusable, actionable step-level task memories that can guide future agent executions.
  Focus on identifying specific patterns, techniques, and decision points that contributed to success.

  ANALYSIS FRAMEWORK:
  ● STEP PATTERN ANALYSIS: Identify the specific sequence of actions that led to success
  ● DECISION POINTS: Highlight critical decisions made during these steps
  ● TECHNIQUE EFFECTIVENESS: Analyze why specific approaches worked well
  ● REUSABILITY: Extract patterns that can be applied to similar scenarios

  EXTRACTION PRINCIPLES:
  ● Focus on TRANSFERABLE TECHNIQUES and decision frameworks
  ● Frame insights as actionable guidelines and best practices

  # Original Query
  {query}

  # Step Sequence Analysis
  {step_sequence}

  # Context Information
  {context}

  # Outcome
  This step sequence was part of a {outcome} trajectory.

  OUTPUT FORMAT:
  Generate 1-3 step-level success insights as JSON objects:
  ```json
  [
    {{
      "when_to_use": "Specific conditions when this step pattern should be applied",
      "experience": "Detailed description of the successful step pattern and why it works",
      "tags": ["relevant", "keywords", "for", "categorization"],
      "confidence": 0.8,
      "step_type": "reasoning|action|observation|decision",
      "tools_used": ["list", "of", "tools"]
    }}
  ]
  ```