Universal Data Analyst (通用数据分析专家)#

Introduction#

An intelligent data analysis skill based on Data Ontology. Unlike keyword-based approaches, this skill uses LLM reasoning for every analysis, automatically identifying data types, selecting analysis methods, generating scripts, and outputting reports.

Supports both economic data (retail, subscription, finance, etc.) and non-economic data (scientific measurements, social networks, text, etc.), handling multiple formats including CSV, Excel, Parquet, JSON, and more.

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How to Trigger#

Simply upload a data file or send any of these types of messages:

• "Help me analyze this data"
• "What patterns are in this CSV?"
• "Explore this dataset"
• "Check the data quality for me"
• Directly upload .csv / .xlsx / .parquet / .json files

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Core Design: Four-Layer Analysis Framework#

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Layer 1: Data Ontology
        ↓  What kind of existence is this? Entity type? Generation mechanism?
Layer 2: Problem Typology
        ↓  Descriptive / Diagnostic / Predictive / Prescriptive / Causal?
Layer 3: Methodology Mapping
        ↓  Match domain-recognized analysis frameworks
Layer 4: Validation & Output
           Data quality report + Analysis scripts + HTML/MD reports

Each layer invokes LLM reasoning without any hardcoded rules.

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Analysis Workflow (7 Steps)#

StepContentDescription1Data LoadingAuto-recognize formats, support multiple file types2Ontology RecognitionLLM judges entity type and generation mechanism3Quality ValidationAuto-detect missing values, outliers, duplicates, output quality score4Plan GenerationLLM selects analysis framework and path based on user intent5Script GenerationLLM generates executable Python analysis scripts6Execute AnalysisRun scripts, generate charts and numerical results7Comprehensive ReportOutput HTML + Markdown dual-format reports

Flow Health Monitoring (NEW)#

Each step has status tracking and error handling:

• Step Dependency Check - Automatically prevents subsequent steps when prerequisites fail
• Clear Error Messages - Provides explicit failure reasons and fix suggestions
• Flow Health Report - Outputs complete execution status and issue summary

If a step fails, you'll see:

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⚠️ Flow Interrupted!
   Reason: Critical step 'Data Loading' failed: Encoding error

Fix Suggestions:
  1. File encoding may not be UTF-8, try manually specifying encoding parameter
  2. Common Chinese encodings: gbk, gb2312, gb18030

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Supported Data Types#

Economic Data#

Data CharacteristicsRecognized AsAuto-matched FrameworkOrders + Price + SKURetail EconomyValue Chain / ABC-XYZ / RFMUser + Subscription Cycle + ChurnSubscription EconomyLTV / Cohort / Retention CurvesClick / Add-to-cart / Purchase EventsAttention EconomyFunnel Analysis / AARRRGMV + Platform MatchingCommission EconomyTwo-sided Network Effects / Unit EconomicsPosition + Skills + SalaryLabor MarketSkill Premium / Experience ElasticityOHLCV Price DataFinancial Time SeriesTechnical Analysis / Volatility Models

Non-Economic Data#

Data TypeAuto-matched FrameworkSensors / Time Series ContinuousTime Series Decomposition, Extreme Value AnalysisSocial / Network RelationshipCentrality Analysis, Community DetectionGeographic / SpatialSpatial Autocorrelation, Hotspot AnalysisText CorpusTopic Modeling, Sentiment AnalysisBiomedicalSurvival Analysis, Differential Expression

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Supported File Formats#

• CSV / TSV ( .csv, .tsv, .txt) - Auto encoding detection, supports utf-8, gbk, latin1, etc.
• Excel ( .xlsx, .xls)
• Parquet ( .parquet, .pq)
• JSON ( .json)
• SQL Database (via connection string)

Encoding Fault Tolerance#

CSV loading automatically tries multiple encodings:

• Auto encoding detection (if chardet library available)
• Fallback encodings: utf-8, utf-8-sig, gbk, gb2312, gb18030, latin1, etc.
• Engine fallback: Auto-switches to Python engine when C engine fails, skipping corrupted rows

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Output Contents#

Each analysis generates:

text

session_YYYYMMDD_HHMMSS/
├── step2_ontology_prompt.txt     # Ontology recognition prompts (reusable)
├── step3_validation_report.json  # Data quality report
├── step3_cleaning_report.txt     # Data cleaning recommendations
├── step4_planning_prompt.txt     # Analysis planning prompts (reusable)
├── step5_script_prompt.txt       # Script generation prompts (reusable)
├── analysis_report.html          # Comprehensive HTML report (with charts)
├── analysis_report.md            # Markdown report
└── charts/                       # All analysis charts (PNG)

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Usage Examples#

Example 1: Analyzing E-commerce Sales Data#

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User: Help me analyze this sales data, want to know which products sell well and which customers are high-value

[Upload orders.csv]

Skill automatically:

1. Recognizes as "Retail Economy × Transaction/Event Data"
2. Selects RFM Customer Value Analysis + ABC Product Classification framework
3. Generates and executes analysis scripts
4. Outputs customer segmentation distribution, product sales ranking, RFM heatmap, and HTML report

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Example 2: Analyzing User Behavior Logs#

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User: This is our App's user behavior log, want to see the user conversion funnel

[Upload events.csv]

Skill automatically:

1. Recognizes as "Attention/Conversion Economy × Event Sequence Data"
2. Selects Funnel Analysis + Session Sequence Mining framework
3. Outputs conversion rates at each step, churn node analysis, user path Sankey diagram

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Example 3: Analyzing Meteorological Observation Data#

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User: Help me analyze this weather station observation record, understand temperature and precipitation patterns

[Upload weather.csv]

Skill automatically:

1. Recognizes as "Earth Science × Time Series/Trajectory Data × Instrument Measurement"
2. Selects Time Series Decomposition + Seasonality Analysis + Extreme Value Statistics framework
3. Outputs trend charts, seasonal decomposition charts, outlier reports

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Dependencies#

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pandas >= 1.3
numpy >= 1.21
matplotlib >= 3.4
seaborn >= 0.11
scipy >= 1.7
openpyxl >= 3.0   # Excel support
chardet >= 4.0    # Auto encoding detection (optional but recommended)
pyarrow >= 6.0    # Parquet support (optional)
sqlalchemy >= 1.4 # SQL support (optional)

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Version#

v1.1.0 · Author: Claude · License: CC BY-NC-SA 4.0

v1.1.0 Updates (2026-03-23)#

1. Flow Health Monitoring - Added step status tracking, dependency checks, error messages
2. Enhanced Encoding Fault Tolerance - Auto-try multiple encodings for CSV/TSV (utf-8, gbk, latin1, etc.)
3. Engine Fallback - Auto-switches to Python engine when C engine fails, skipping corrupted rows

v1.0.0#

• Initial version: Four-layer analysis framework + 7-step analysis workflow