universal-data-analyst-en

Performs automated, LLM-driven data analysis including loading, validation, method selection, script generation, execution, and comprehensive reporting for d...

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)

Step	Content	Description
1	Data Loading	Auto-recognize formats, support multiple file types
2	Ontology Recognition	LLM judges entity type and generation mechanism
3	Quality Validation	Auto-detect missing values, outliers, duplicates, output quality score
4	Plan Generation	LLM selects analysis framework and path based on user intent
5	Script Generation	LLM generates executable Python analysis scripts
6	Execute Analysis	Run scripts, generate charts and numerical results
7	Comprehensive Report	Output 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 Characteristics	Recognized As	Auto-matched Framework
Orders + Price + SKU	Retail Economy	Value Chain / ABC-XYZ / RFM
User + Subscription Cycle + Churn	Subscription Economy	LTV / Cohort / Retention Curves
Click / Add-to-cart / Purchase Events	Attention Economy	Funnel Analysis / AARRR
GMV + Platform Matching	Commission Economy	Two-sided Network Effects / Unit Economics
Position + Skills + Salary	Labor Market	Skill Premium / Experience Elasticity
OHLCV Price Data	Financial Time Series	Technical Analysis / Volatility Models

Non-Economic Data

Data Type	Auto-matched Framework
Sensors / Time Series Continuous	Time Series Decomposition, Extreme Value Analysis
Social / Network Relationship	Centrality Analysis, Community Detection
Geographic / Spatial	Spatial Autocorrelation, Hotspot Analysis
Text Corpus	Topic Modeling, Sentiment Analysis
Biomedical	Survival 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:

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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