{% macro tidy(name) -%}
  {{ name.replace('_', ' ')}}
{%- endmacro %}


===Task===

Your task is to generate a comprehensive memory insight report based on the provided data analysis. The report should include four distinct sections that capture different aspects of the user's memory patterns and characteristics.

{% if language == "zh" %}
**重要：请使用中文生成记忆洞察报告内容。**
{% else %}
**Important: Please generate the memory insight report content in English.**
{% endif %}


===Inputs===
{% if domain_distribution %}
- 核心领域分布: {{ domain_distribution }}
{% endif %}
{% if active_periods %}
- 活跃时段: {{ active_periods }}
{% endif %}
{% if social_connections %}
- 社交关联: {{ social_connections }}
{% endif %}


===Report Generation Requirements===

**General Guidelines:**
1. Base your analysis ONLY on the provided data - do not speculate or fabricate information
2. Use objective third-person descriptions with a professional and analytical tone
3. Avoid excessive adjectives and empty phrases
4. Strictly follow the output format specified below
5. If a dimension lacks data, skip that section or provide a brief note

**Section-Specific Requirements:**

{% if language == "zh" %}
1. **总体概述** (100-150字)
   - 重点：基于交互日志对用户档案进行整体分析
   - 描述用户的主要角色、工作网络和协作精神
   - 使用专业、数据驱动的语言风格
   - 示例参考："通过对156次交互日志的深度分析，系统发现张三是一位主要从事用户档案和数据分析的产品经理。他的工作网络体现出鲜明的目标导向和团队协作精神。"

2. **行为模式** (80-120字)
   - 重点：工作模式、时间规律和行为特征
   - 描述每周工作模式和时间偏好
   - 使用客观、分析性的语言
   - 示例参考："张三的工作模式呈现出鲜明的周期性：周一通常用于规划和会议，周三周四专注于产品设计和用户研究，周五进行总结和复盘。他倾向于在上午进行头脑风暴，下午处理执行性工作。"

3. **关键发现** (3-4个要点，每个30-50字)
   - 重点：关于用户行为和偏好的具体、有洞察力的观察
   - 使用项目符号（•）格式
   - 每个发现应具体且有数据支持
   - 示例参考：
     "• 在产品决策中，张三总是优先考虑用户反应，这在68%的决策记录中得到体现
     • 他善于使用数据可视化工具来支持论点，这种习惯在项目管理中发挥了重要作用
     • 团队成员对他的评价中，"思路清晰"和"思路敏捷"两个关键词出现频率最高
     • 他对AI机器学习领域保持持续关注，近3个月参加了7次相关培训"

4. **成长轨迹** (100-150字)
   - 重点：用户的成长历程、关键里程碑和能力提升
   - 按时间顺序组织内容
   - 突出角色变化和成就
   - 使用积极、鼓励的语气
   - 示例参考："从入职时的产品经理成长为高级产品经理，张三在产品规划、团队管理和技术理解三个方面都有显著提升。特别是在最近一年，他开始独立主导更复杂的项目，展现出更强的战略思维能力。他的成长轨迹显示出对新技术的持续学习和对产品思维的不断深化。"
{% else %}
1. **Overview** (100-150 words)
   - Focus on: Overall analysis of user profile based on interaction logs
   - Describe the user's main role, work network, and collaboration spirit
   - Use professional, data-driven language style
   - Example reference: "Through in-depth analysis of 156 interaction logs, the system identified Zhang San as a product manager primarily focused on user profiling and data analysis. His work network demonstrates a clear goal-oriented approach and team collaboration spirit."

2. **Behavior Pattern** (80-120 words)
   - Focus on: Work patterns, time regularity, and behavioral characteristics
   - Describe weekly work patterns and time preferences
   - Use objective, analytical language
   - Example reference: "Zhang San's work pattern shows distinct periodicity: Mondays are typically used for planning and meetings, Wednesdays and Thursdays focus on product design and user research, and Fridays are for summary and review. He tends to brainstorm in the morning and handle execution tasks in the afternoon."

3. **Key Findings** (3-4 bullet points, 30-50 words each)
   - Focus on: Specific, insightful observations about user behavior and preferences
   - Use bullet points (•) format
   - Each finding should be concrete and data-supported
   - Example reference:
     "• In product decisions, Zhang San always prioritizes user feedback, as evidenced in 68% of decision records
     • He excels at using data visualization tools to support arguments, a habit that plays an important role in project management
     • Among team member evaluations, 'clear thinking' and 'quick thinking' are the most frequently mentioned keywords
     • He maintains continuous attention to AI and machine learning, attending 7 related training sessions in the past 3 months"

4. **Growth Trajectory** (100-150 words)
   - Focus on: User's growth journey, key milestones, and capability improvements
   - Organize content chronologically
   - Highlight role changes and achievements
   - Use positive, encouraging tone
   - Example reference: "Growing from a product manager at entry to a senior product manager, Zhang San has shown significant improvement in product planning, team management, and technical understanding. Especially in the past year, he has begun to independently lead more complex projects, demonstrating stronger strategic thinking capabilities. His growth trajectory shows continuous learning of new technologies and deepening of product thinking."
{% endif %}


===Output Format (MUST STRICTLY FOLLOW)===

{% if language == "zh" %}
【总体概述】
[100-150字，基于交互分析描述用户整体档案和工作网络]

【行为模式】
[80-120字，描述工作模式、时间规律和行为特征]

【关键发现】
• [第一个关键发现，有数据支持，30-50字]
• [第二个关键发现，有数据支持，30-50字]
• [第三个关键发现，有数据支持，30-50字]
• [第四个关键发现，有数据支持，30-50字]

【成长轨迹】
[100-150字，描述成长历程、关键里程碑和能力提升]
{% else %}
【Overview】
[100-150 words describing overall user profile and work network based on interaction analysis]

【Behavior Pattern】
[80-120 words describing work patterns, time regularity, and behavioral characteristics]

【Key Findings】
• [First key finding with data support, 30-50 words]
• [Second key finding with data support, 30-50 words]
• [Third key finding with data support, 30-50 words]
• [Fourth key finding with data support, 30-50 words]

【Growth Trajectory】
[100-150 words describing growth journey, milestones, and capability improvements]
{% endif %}


===Example===

{% if language == "zh" %}
Example Input:
- 核心领域分布: 产品管理(38%), 数据分析(24%), 团队协作(21%)
- 活跃时段: 用户在每年的 4 和 10 月最为活跃
- 社交关联: 与用户"李明"拥有最多共同记忆(47条)，时间范围主要在 2020-2023

Example Output:
【总体概述】
通过对156次交互日志的深度分析，系统发现张三是一位主要从事用户档案和数据分析的产品经理。他的工作网络体现出鲜明的目标导向和团队协作精神，在产品管理、数据分析和团队协作三个领域都有深入的实践。

【行为模式】
张三的工作模式呈现出鲜明的周期性：周一通常用于规划和会议，周三周四专注于产品设计和用户研究，周五进行总结和复盘。他倾向于在上午进行头脑风暴，下午处理执行性工作。每年4月和10月是他最活跃的时期。

【关键发现】
• 在产品决策中，张三总是优先考虑用户反应，这在68%的决策记录中得到体现
• 他善于使用数据可视化工具来支持论点，这种习惯在项目管理中发挥了重要作用
• 团队成员对他的评价中，"思路清晰"和"思路敏捷"两个关键词出现频率最高
• 他对AI机器学习领域保持持续关注，近3个月参加了7次相关培训

【成长轨迹】
从入职时的产品经理成长为高级产品经理，张三在产品规划、团队管理和技术理解三个方面都有显著提升。特别是在最近一年，他开始独立主导更复杂的项目，展现出更强的战略思维能力。他与李明的47条共同记忆见证了他的成长历程。
{% else %}
Example Input:
- Core Domain Distribution: Product Management (38%), Data Analysis (24%), Team Collaboration (21%)
- Active Periods: User is most active in April and October each year
- Social Connections: Has the most shared memories (47 entries) with user "Li Ming", primarily during 2020-2023

Example Output:
【Overview】
Through in-depth analysis of 156 interaction logs, the system identified Zhang San as a product manager primarily focused on user profiling and data analysis. His work network demonstrates a clear goal-oriented approach and team collaboration spirit, with deep practical experience in product management, data analysis, and team collaboration.

【Behavior Pattern】
Zhang San's work pattern shows distinct periodicity: Mondays are typically used for planning and meetings, Wednesdays and Thursdays focus on product design and user research, and Fridays are for summary and review. He tends to brainstorm in the morning and handle execution tasks in the afternoon. April and October are his most active periods each year.

【Key Findings】
• In product decisions, Zhang San always prioritizes user feedback, as evidenced in 68% of decision records
• He excels at using data visualization tools to support arguments, a habit that plays an important role in project management
• Among team member evaluations, "clear thinking" and "quick thinking" are the most frequently mentioned keywords
• He maintains continuous attention to AI and machine learning, attending 7 related training sessions in the past 3 months

【Growth Trajectory】
Growing from a product manager at entry to a senior product manager, Zhang San has shown significant improvement in product planning, team management, and technical understanding. Especially in the past year, he has begun to independently lead more complex projects, demonstrating stronger strategic thinking capabilities. His 47 shared memories with Li Ming bear witness to his growth journey.
{% endif %}

===End of Example===


===Reflection Process===

After generating the report, perform the following self-review steps:

**Step 1: Data Grounding Check**
- Verify all statements are supported by the provided data
- Ensure no fabricated or speculated information is included
- Confirm all claims can be traced back to the input data

**Step 2: Format Compliance**
- Verify each section follows the specified format with section headers
- Check character count limits for each section
- Ensure proper use of section markers (【】)
- Verify bullet points format for Key Findings section

**Step 3: Tone and Style Review**
- Confirm objective third-person perspective is maintained
- Check for excessive adjectives or empty phrases
- Verify professional and analytical tone throughout

**Step 4: Completeness Check**
- Ensure all four sections are present and complete
- Verify each section addresses its specific focus area
- Confirm the report provides actionable insights


===Output Requirements===

{% if language == "zh" %}
**语言要求：**
- 输出语言必须始终为简体中文
- 所有章节内容必须使用中文
- 章节标题必须使用指定的中文格式：【总体概述】【行为模式】【关键发现】【成长轨迹】

**格式要求：**
- 每个章节必须以标题开头，标题独占一行
- 内容紧跟标题之后
- 章节之间用空行分隔
- 关键发现章节必须使用项目符号（•）
- 严格遵守每个章节的字数限制

**内容要求：**
- 仅使用提供的数据点
- 不得捏造或推测信息
- 如果某个章节数据不足，请简要说明或跳过
- 全文保持专业、分析性的语气
{% else %}
**LANGUAGE REQUIREMENT:**
- The output language must ALWAYS be English
- All section content must be in English
- Section headers must use the specified English format: 【Overview】【Behavior Pattern】【Key Findings】【Growth Trajectory】

**FORMAT REQUIREMENT:**
- Each section must start with its header on a new line
- Content follows immediately after the header
- Sections are separated by blank lines
- Key Findings section must use bullet points (•)
- Strictly adhere to word limits for each section

**CONTENT REQUIREMENT:**
- Only use provided data points
- Do not fabricate or speculate information
- If data is insufficient for a section, provide a brief note or skip
- Maintain professional, analytical tone throughout
{% endif %}
