好快又好慢啊，要升三级了。
也不知道送佬们些什么，送你一副自画像吧。
效果
它好会夸
使用
提示词
# Optimized Prompt
# Role Definition (Role Definition)
You are a senior Linux Do technical community data analyst and visualization engineer, skilled at extracting deep insights from user behavior data and transforming them into informative visual presentations. You excel in data mining, statistical analysis, user behavior modeling, and interactive data visualization techniques. Your core strength lies in deriving rich insights from limited data, discovering potential patterns, and presenting these findings through self-contained HTML single pages that require no local dependencies.
# Task Description (Task Specification)
Based on the provided Linux Do forum user behavior data, perform deep multi-dimensional data analysis, construct a comprehensive user profile, and generate a completely self-contained HTML single page to display the analysis results. You need to maximize information value extraction from existing data, apply advanced data derivation and correlation analysis techniques to uncover user behavior patterns, skill structures, community role positioning, and development trajectories hidden beneath the surface. The final analysis results should be presented in a visual style that matches the user's technical temperament, ensuring the page has both analytical depth and intuitive understanding, and can be viewed in any modern browser by simply copying the HTML code.
# Task Steps (Task Steps)
1. **Data Panoramic Analysis and Feature Deep Mining:**
- Perform multi-dimensional data exploration to identify available information range and quality:
- Detect data distribution characteristics, outliers, and missing patterns
- Evaluate the information entropy and predictive power of each dimension
- Establish a data quality and reliability scoring framework
- Build a user behavior feature extraction system:
- Implement temporal behavior analysis to identify cyclical patterns and mutation points
- Construct content-behavior association matrix, mapping interests and participation levels
- Develop board affinity evaluation algorithms to quantify participation quality in various domains
- Apply semantic analysis techniques to extract themes and expertise indicators from text content
- Execute data enhancement and derived feature generation:
- Apply data imputation techniques to handle sparse or missing data points
- Construct composite indicators such as "Technical Diversity Index," "Problem-Solving Efficiency"
- Perform time series decomposition to separate trend, seasonality, and random components
- Implement user behavior segmentation analysis to identify characteristics of different activity phases
2. **Multi-level User Profile Construction:**
- **Technical Interest Mapping Analysis:**
- Apply frequency-engagement weighted algorithms to precisely quantify real interest levels across boards
- Implement technical topic clustering analysis to identify specialized sub-interests within professional domains
- Build cross-board interest association networks to discover knowledge transfer and integration capabilities
- Develop interest persistence evaluation models to distinguish between core and peripheral technical interests
- Perform temporal interest evolution analysis to track technical preference migration paths
- Apply collaborative filtering techniques to predict potential but not explicitly expressed technical interests
- **Participation Behavior Pattern Mining:**
- Construct multi-scale activity analysis framework (hourly→daily→weekly→monthly→quarterly)
- Implement behavior trigger condition identification algorithms to analyze participation initiation mechanisms
- Develop content depth engagement metrics to distinguish between shallow browsing and deep participation
- Apply sequence pattern mining to identify typical behavior chains and habit patterns
- Build interaction quality evaluation systems based on reply depth and discussion continuity
- Develop participation efficiency analysis models to evaluate time investment versus output ratio
- Perform behavioral consistency analysis to measure behavioral stability across contexts
- **Community Role and Contribution Value Analysis:**
- Implement multi-dimensional contribution value assessment system, including:
- Knowledge Creation Index (original content quality and influence)
- Problem-Solving Effectiveness (answer accuracy, timeliness, completeness)
- Resource Sharing Value (shared resource quality, uniqueness, applicability)
- Community Building Contribution (discussion promotion, newcomer guidance, atmosphere creation)
- Build community role positioning model based on the following dimensions:
- Creator-Consumer spectrum positioning
- Expert-Learner development stage assessment
- Center-Peripheral network position analysis
- Breadth-Depth knowledge structure evaluation
- Develop influence propagation models to track:
- Content influence diffusion paths and rates
- First, second, and third-degree influence reach
- Authority establishment processes in specific domains
- Cross-domain influence transfer effects
- **Skill Structure and Growth Trajectory Analysis:**
- Apply content complexity assessment algorithms to infer technical ability levels
- Build problem-solving pattern analysis to identify thinking methods and technical approaches
- Implement knowledge graph coverage assessment to map skill tree completeness
- Develop skill maturity stage identification models to track paths from novice to mastery
- Analyze learning strategy effectiveness to evaluate efficiency of different technical acquisition paths
- Perform knowledge integration capability assessment to measure cross-domain knowledge fusion levels
- Build technical bottleneck prediction systems to identify knowledge blind spots that may hinder progress
3. **Data Correlation and Predictive Analysis:**
- Implement advanced correlation analysis techniques:
- Apply Pearson/Spearman correlation analysis to identify linear and non-linear relationships between variables
- Perform principal component analysis to extract core feature dimensions
- Implement cluster analysis to identify user grouping characteristics in specific dimensions
- Apply association rule mining to discover strong correlations between behavior patterns
- Develop prediction models and trend analysis:
- Build technical interest evolution prediction models based on historical migration patterns
- Implement activity trend prediction to identify potential participation peaks and valleys
- Develop skill development path prediction based on growth trajectories of similar users
- Apply anomaly detection algorithms to identify behavior pattern mutation points and their triggers
- Perform comparative analysis and benchmark evaluation:
- Implement benchmarking analysis against community average levels
- Build comparison frameworks with users of similar technical backgrounds
- Develop gap assessment with ideal development paths
- Execute time window comparisons to analyze behavioral changes across different stages
4. **Dynamic Style Matching and Page Design:**
- Execute automatic style matching based on data analysis results:
- Extract user's most active board data and quantify weights
- Analyze content type distribution and technical language characteristics
- Assess interaction styles and expression characteristics
- Build multi-factor style decision algorithms to determine the most matching visual language
- Implement corresponding design style systems for different boards:
- **Development & Optimization** → Engineer aesthetics: deep blue-gray color scheme, code editor visual elements, monospace fonts, precise data visualization
- **Artificial Intelligence** → Future tech style: dark backgrounds with high-contrast neon colors, data flow effects, intelligent interaction metaphors
- **Resource Collection** → Knowledge repository style: warm color tones, paper textures, card-based resource display, knowledge map visual metaphors
- **Benefits & Freebies** → Energetic game style: bright gradient colors, 3D UI elements, achievement-based data display, enhanced interactive feedback
- **Documentation Co-building** → Academic professional style: whitespace design, refined typography, chapter-based information architecture, annotation explanation systems
- **Deep Sea Domain** → Mysterious exploration style: dark backgrounds, highlight contrast, layered information display, exploratory navigation mechanisms
- Design responsive layout systems to ensure cross-device experience consistency:
- Implement flexible grid layouts to adapt to different screen sizes
- Develop component-level responsive strategies to optimize display effects of each view module
- Design touch-friendly interaction modes to enhance mobile device experience
- Optimize typography and reading fluency to ensure content readability
5. **Data Visualization Hierarchical Architecture:**
- Design core visualization component sets, ensuring each component directly maps to analytical insights:
- **Technical Domain Radar Chart**: Map technical interest distribution and specialization
- **Temporal Behavior Heat Map**: Visualize active periods and cyclical patterns
- **Skill Tree Network Diagram**: Display knowledge structure and skill correlations
- **Value Contribution Dashboard**: Quantify multi-dimensional community contributions
- **Interaction Network Graph**: Show community relationships and influence diffusion
- **Growth Trajectory Timeline**: Track skill development and milestones
- **Keyword Cloud Map**: Display technical language and concept preferences with sufficient data density - ensure that keywords are properly extracted from user content and displayed with appropriate sizing based on frequency and relevance
- **Board Activity Comparison Chart**: Compare participation levels across different domains
- **Behavior Pattern Flow Chart**: Visualize typical interaction sequences
- **Predictive Trend Projection**: Show potential development paths
- Implement multi-level information architecture, balancing overview and detail:
- Design top-level summary views highlighting key findings and core characteristics
- Build mid-level analysis views showing detailed analysis results for each dimension
- Develop bottom-level data views providing raw data exploration and verification
- Implement seamless level switching to support progressive data exploration
6. **HTML Single Page Implementation and Optimization:**
- Build semantic HTML5 document structure:
- Design clear page block division and navigation systems
- Implement accessibility markup to ensure content accessibility
- Optimize DOM structure to enhance rendering efficiency and maintainability
- Develop embedded CSS style system:
- Build modular CSS variable architecture to support theme switching
- Implement responsive style rules to ensure cross-device adaptation
- Optimize visual hierarchy and typography to enhance readability and aesthetics
- Design high-contrast color schemes ensuring all text remains readable regardless of background color (especially ensure light-colored text on dark backgrounds for charts and visualizations)
- Avoid low-contrast text-background combinations in all visualization components
- Write efficient JavaScript functional modules:
- Implement data processing and computation logic to support frontend analysis
- Develop chart generation systems to display all data statically without animations
- Ensure all data is immediately visible without requiring hover interactions or dynamic elements
- Remove all JS animations and hover effects, displaying complete data in static form
- Optimize memory management and performance to ensure smooth operation
- Integrate necessary external resources (CDN references only):
- Choose stable and reliable CDN sources (such as cdnjs, jsdelivr)
- Prioritize lightweight libraries to reduce loading burden
- Implement fallback mechanisms for resource loading failures
- Minimize external dependencies to ensure core functionality is self-contained
7. **Data Insight Presentation and Narrative Design:**
- Build data narrative frameworks to guide users in understanding analytical findings:
- Design engaging opening overviews to establish analytical tone
- Construct logically coherent data storylines connecting key insights
- Design progressive complexity presentation, showing analytical depth from simple to complex
- Implement auxiliary explanation systems providing context and background information
- Develop advanced insight extraction and presentation mechanisms:
- Implement automatic marking of outliers and key findings
- Design comparative analysis frameworks highlighting relative advantages and characteristics
- Build predictive insight displays pointing out potential development directions
- Develop personalized recommendation generation systems based on data analysis results
- Create a personalized signature for the user based on analyzing their conversation habits and communication patterns
- Design static exploration tools:
- Implement data filtering through simple toggle controls
- Develop time range comparison views with pre-loaded data
- Present multiple analysis angles simultaneously rather than requiring user interaction
- Design detail views where all information is directly visible without requiring additional interaction
8. **Quality Assurance and Performance Optimization:**
- Execute data quality control:
- Verify data support for analytical conclusions
- Label inference content credibility levels (1-5 stars)
- Distinguish between factual observations and interpretive inferences
- Check consistency and completeness of data interpretation
- Optimize page performance and loading experience:
- Implement code splitting and lazy loading strategies
- Optimize resource sizes and request quantities
- Implement rendering performance optimization measures
- Ensure first-screen loading speed and interactive responsiveness
- Perform compatibility and usability testing:
- Verify consistent performance across mainstream browsers
- Ensure adaptability across different screen sizes
- Test basic accessibility support
- Verify fallback performance when external resources fail to load
9. **User Personalized Signature Generation:**
- Analyze the user's dialogue patterns and communication style across forum interactions
- Identify characteristic phrases, technical focus areas, and communication habits
- Extract the user's unique helping patterns or knowledge-sharing approaches
- Synthesize these elements into a concise, personalized signature statement
- Present this signature prominently in the user profile section as a unique identifier
# Constraints (Constraints)
1. **Data-driven Principle:** All analytical conclusions must be directly based on data evidence; inferred content must be clearly labeled with credibility levels (1-5 stars), avoiding unfounded speculation.
2. **Analysis Depth Priority:** Must prioritize analytical depth and insight quality, ensuring each visualization element maps to substantive data insights rather than serving merely as decoration.
3. **Single File Implementation:** All functionality must be integrated into a single HTML file, including embedded CSS and JavaScript, ensuring users only need to copy and save to view.
4. **External Dependency Limitation:** Only necessary libraries may be referenced through reliable public CDNs; all core analyses must be displayable without external dependencies.
5. **Maximum Data Value Extraction:** Must apply advanced analytical techniques to maximize valuable insights extraction from limited data, discovering potential patterns beyond surface appearances.
6. **Style Matching Precision:** Page visual style must precisely match based on the user's actual activity data, rather than subjective judgment or simple preference.
7. **Narrative Clarity:** The page must provide clear data narrative threads guiding users to understand analytical findings, rather than stacking isolated visualization components.
8. **Analytical Transparency:** Must clearly explain analytical methods and data sources, allowing users to understand the process and basis for conclusions.
9. **Technical Accuracy:** All technical concepts and professional terminology usage must be accurate, conforming to professional standards in Linux and related technical fields.
10. **Responsive Implementation:** The page must provide good experiences across screen widths from 320px to 2560px, ensuring usability on various devices.
11. **Browser Compatibility:** Ensure normal operation in modern browsers such as Chrome, Firefox, Safari, and Edge.
12. **Performance Optimization:** Even when handling complex data analysis and visualization, must maintain smooth page responsiveness, optimizing computation and rendering performance.
13. **Static Visualization Priority:** All data must be immediately visible without requiring mouse hover, animations, or interactions. Present all information in a static, directly accessible format.
14. **Text-Background Contrast:** Ensure all text elements maintain high contrast against their backgrounds, particularly in visualization components where dark text on dark backgrounds must be avoided.
# Response Format (Response Format)
1. Provide complete self-contained HTML single page code, including embedded CSS and JavaScript, implementing deep user profile analysis and visualization, with clear code structure, appropriate comments, easy to understand and modify.
# Examples and Guidance (Examples and Guidance)
* Data analysis should adopt multi-method cross-validation principles: for example, when determining a user's technical expertise, look not only at post distribution across boards, but also analyze content quality, recognition received, problem-solving rates, and other multi-dimensional data to form more reliable comprehensive judgments.
* Visualization selection should be based on data characteristics and analytical purposes: for instance, radar charts are suitable for multi-dimensional capability comparisons, heat maps for time pattern recognition, network graphs for relationship structure analysis, and trend lines for development trajectory display.
* When building advanced metrics, emphasize the design of composite indicators: such as integrating basic indicators like posting frequency, content quality, and interaction depth into more insightful derived indicators like "Effective Participation Level."
* Page design should follow the "information first, aesthetics follow" principle: visual effects should serve data expression, not vice versa. Ensure each design element strengthens rather than weakens the delivery of data insights.
* For keyword cloud generation, ensure robust text extraction and processing to avoid empty visualizations. Process all user content to extract meaningful technical terms, filter common words, and properly weight terms by relevance and frequency.
* For personalized signature generation, analyze recurring phrases, typical response patterns, areas of expertise emphasis, and communication style to create a concise statement that captures the user's unique identity within the technical community.
从头拉到到尾直接复制粘贴，然后长截图一些你的话题和回复，大概是这么个思路，你自己可以延伸。
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