data-analysis

name

data-analysis

description

A full analysis pipeline where an agent team collaborates to perform exploratory data analysis (EDA), data cleaning, statistical analysis, visualization, and report writing. Use this skill for 'analyze this data', 'do EDA', 'exploratory analysis', 'statistical analysis', 'data visualization', 'write an analysis report', 'analyze CSV', 'extract data insights', 'data cleaning', 'outlier analysis', and other data analysis tasks. Note: real-time data streaming, ML model training/deployment, and BI dashboard server construction are outside this skill's scope.

Data Analysis — Full Data Analysis Pipeline

An agent team collaborates to perform the full data lifecycle: exploration → cleaning → analysis → visualization → reporting.

Execution Mode

Agent Team — 5 members communicate directly via SendMessage and cross-validate.

Agent Composition

Agent	File	Role	Type
explorer	.claude/agents/explorer.md	Exploratory analysis, data profiling	general-purpose
cleaner	.claude/agents/cleaner.md	Data cleaning, transformation pipeline	general-purpose
analyst	.claude/agents/analyst.md	Statistical analysis, insight derivation	general-purpose
visualizer	.claude/agents/visualizer.md	Chart design, visualization code generation	general-purpose
reporter	.claude/agents/reporter.md	Final report writing, quality verification	general-purpose

Workflow

Phase 1: Preparation (Orchestrator performs directly)

1. Extract from user input:
   • Data Source: File path, format (CSV/Excel/JSON/DB), size
   • Analysis Purpose: Business questions, hypotheses, expected results
   • Constraints (optional): Time, specific analysis techniques, reporting audience
   • Domain Information (optional): Industry, variable meanings, business context
2. Create _workspace/ directory and _workspace/scripts/ subdirectory
3. Organize input and save to _workspace/00_input.md
4. Copy data files to _workspace/data/
5. If existing files are present, copy to _workspace/ and skip the corresponding Phase
6. Determine execution mode based on request scope

Phase 2: Team Assembly and Execution

Order	Task	Owner	Dependencies	Output
1	Exploratory Analysis	explorer	None	01_exploration_report.md
2	Data Cleaning	cleaner	Task 1	02_cleaning_log.md, scripts/02_cleaning.py
3a	Statistical Analysis	analyst	Task 2	03_analysis_results.md, scripts/03_analysis.py
3b	EDA Visualization	visualizer	Task 1	04_visualizations.md (EDA portion)
4	Analysis Result Visualization	visualizer	Task 3a	04_visualizations.md (analysis portion), scripts/04_viz_*.py
5	Final Report	reporter	Tasks 3a, 4	05_final_report.md

Tasks 3a (analysis) and 3b (EDA visualization) run in parallel.

Inter-team communication flow:

• explorer completes → Sends cleaning recommendations to cleaner, analysis suggestions to analyst, distribution visualization targets to visualizer
• cleaner completes → Sends cleaned data location and transformation history to analyst
• analyst completes → Sends visualization requests to visualizer, insights to reporter
• visualizer completes → Sends visualization list to reporter
• reporter cross-validates all outputs. Sends correction requests when inconsistencies are found (up to 2 times)

Phase 3: Integration and Final Outputs

1. Check all files in _workspace/
2. Verify that all required corrections from reporter have been addressed
3. Report final summary to the user:
   • Exploration Report — 01_exploration_report.md
   • Cleaning Log — 02_cleaning_log.md
   • Analysis Results — 03_analysis_results.md
   • Visualizations — 04_visualizations.md
   • Final Report — 05_final_report.md
   • Reproducible Scripts — scripts/ directory

Scale-Based Modes

User Request Pattern	Execution Mode	Agents Deployed
"Analyze the data", "Full analysis"	Full Pipeline	All 5
"Just do EDA", "Data exploration"	Exploration Mode	explorer + visualizer
"Clean the data", "Data cleaning"	Cleaning Mode	explorer + cleaner
"Statistical analysis only", "Hypothesis testing"	Analysis Mode	analyst + visualizer + reporter
"Visualization only", "Draw charts"	Visualization Mode	visualizer only
"Write analysis report" (existing analysis)	Report Mode	reporter only

Leveraging existing files: If already cleaned data exists, skip explorer and cleaner. If analysis results exist, skip analyst and proceed with visualization and reporting only.

Data Transfer Protocol

Strategy	Method	Purpose
File-based	_workspace/ directory	Primary outputs and data storage
Message-based	SendMessage	Key information transfer, correction requests
Code-based	_workspace/scripts/	Reproducible analysis scripts

File naming convention: {order}_{output}.{extension}

Error Handling

Error Type	Strategy
File read failure	Try encodings sequentially (UTF-8→CP949→EUC-KR→Latin-1), auto-detect delimiter
Large data (>1GB)	Analyze with sampling, chunk processing, use dask for full statistics
Analysis assumptions not met	Auto-switch to nonparametric alternatives, state rationale in report
Font rendering issues	Auto-insert OS-specific font configuration code
Agent failure	1 retry, proceed without that output if failed, note omission in report
Reporter finds inconsistency	Send correction request to relevant agent → rework → re-verify (up to 2 times)

Test Scenarios

Normal Flow

Prompt: "Analyze this sales CSV file and find the cause of the sales decline" Expected Results:

• EDA: Variable profiling, missing/outlier analysis, sales-related variable identification
• Cleaning: Missing value treatment, outlier capping, type conversion log
• Analysis: Time-series decomposition, segment comparison (t-test/ANOVA), correlation analysis
• Visualization: Sales trend line chart, factor comparison bar chart, correlation heatmap
• Report: Top 3 decline causes + recommended actions + executive summary

Existing File Flow

Prompt: "I already have cleaned data. Just do statistical analysis and visualization" + cleaned data file attached Expected Results:

• Copy existing data to _workspace/data/
• Analysis mode: Skip explorer and cleaner, deploy analyst + visualizer + reporter
• Cleaning history recorded based on user-provided information

Error Flow

Prompt: "Analyze this Excel file" (many variables with >50% missing, numerous outliers) Expected Results:

• explorer reports data quality issues in detail
• cleaner presents per-variable treatment strategies with rationale, warning if rows decrease >30%
• analyst states data sufficiency limitations and performs power analysis
• reporter honestly records data quality issues in limitations section

Agent Extension Skills

Skill	Path	Enhanced Agent	Role
statistical-tests-selector	.claude/skills/statistical-tests-selector/skill.md	analyst	Test selection tree, t-test/ANOVA/chi-squared, effect size, power
visualization-chooser	.claude/skills/visualization-chooser/skill.md	visualizer	Chart type matrix, matplotlib/seaborn/plotly patterns, anti-patterns