Data Analysis

Data Analysis - Data Analysis Plugin for Claude Code

A comprehensive data analysis and storytelling skill optimized for financial, SaaS, and RevOps contexts. This plugin provides structured workflows for turning raw data into actionable insights with full transparency on analytical decisions, bias awareness, and progressive disclosure reporting.

Features

• Decision Logging: Track every analytical choice for audit trails and reproducibility
• Bias-Aware Interpretation: Built-in checklists for survivorship bias, Simpson's paradox, and more
• Progressive Disclosure: Slide deck -> detailed report -> full notebook with all decisions documented
• Financial Standards: Excel output with proper formulas, color coding, and zero formula errors
• Dashboard Building: Marimo-based interactive dashboards with KPI cards and filters

Installation

Option 1: Marketplace (Recommended)

# 1. Add the Casper Studios marketplace
/plugin marketplace add Casper-Studios/plugin-marketplace

# 2. Install the plugin
/plugin install data-analysis

Option 2: Git Clone + Local Plugin Directory

# Clone the repository
git clone git@github.com:Casper-Studios/plugin-marketplace.git

# Run Claude Code with the plugin directory
claude --plugin-dir ./plugin-marketplace

Commands

/data-analysis:analyze

Run the full data analysis workflow with decision logging and bias checking.

Workflow Overview

Every analysis follows a 7-phase process:

1. SETUP    → Initialize Marimo notebook (run init_marimo_notebook.py)
2. INGEST   → Load data, document sources and assumptions
3. EXPLORE  → EDA with logged decisions (why this viz, why this filter)
4. MODEL    → If needed, with interpretable-first approach
5. INTERPRET → Apply bias checklist, hedge appropriately
6. WISHLIST → Document data gaps and proxies used
7. OUTPUT   → Generate appropriate tier (slides/report/notebook)

Reference Files

Reference	When to Use
references/metrics.md	Calculating SaaS/RevOps metrics (ARR, MRR, NRR, churn, LTV, CAC)
references/biases.md	Interpretation phase, before finalizing insights
references/data-quality-validator.md	Data quality validation, detecting statistical issues
references/data-cleaning.md	Data quality checks, cleaning patterns
references/datetime-handling.md	Timezone, parsing, fiscal calendars
references/dashboard-patterns.md	Marimo layouts, KPIs, interactivity
references/visualization-guide.md	Choosing chart types, avoiding anti-patterns
references/report-templates.md	Pyramid Principle vs Consulting structure
references/data-wishlisting.md	Documenting gaps, rating proxy quality
references/xlsx-patterns.md	Excel output, financial model standards, formulas
references/pdf-patterns.md	PDF extraction, report creation, manipulation

Scripts

Script	Purpose	Usage
init_marimo_notebook.py	Initialize analysis workspace	python scripts/init_marimo_notebook.py <name>
profile_data.py	Generate data quality report	python scripts/profile_data.py <csv_file>
init_dashboard.py	Scaffold interactive dashboard	python scripts/init_dashboard.py <name>
generate_pptx_summary.py	Create slide deck from findings	python scripts/generate_pptx_summary.py <config.json>
recalc.py	Recalculate Excel formulas	python scripts/recalc.py <xlsx_file>

Technology Stack

Tool	Purpose	Why
Marimo	Notebook environment	Pure Python files, reactive, git-friendly
pandas	Data manipulation	Reliable LLM code generation, mature ecosystem
Matplotlib/Seaborn	Visualization	Publication-quality, static, well-supported
python-pptx	Slide generation	Programmatic PowerPoint creation
openpyxl	Excel files	Formulas, formatting, financial models
pypdf/pdfplumber	PDF handling	Extract text, tables; create reports
reportlab	PDF creation	Professional PDF reports

Example Invocations

"Analyze our ARR trends by segment and identify drivers of growth/churn"
"Build a win rate analysis by deal size and sales rep"
"Create a retention cohort analysis for customers acquired in 2023"
"Project next quarter revenue based on current pipeline"
"Create an executive summary deck of our key SaaS metrics"
"Clean this messy CSV and profile the data quality"
"Build a dashboard to monitor our key SaaS metrics"
"Sanity check these findings before I present them"
"Export this analysis to Excel with proper formulas and formatting"
"Extract the tables from this quarterly report PDF"

Directory Structure

data-analysis/
├── .claude-plugin/
│   ├── plugin.json              # Plugin manifest
│   └── marketplace.json         # Marketplace metadata
├── commands/
│   └── analyze.md               # Main analysis command
├── scripts/
│   ├── init_marimo_notebook.py  # Notebook scaffolding
│   ├── profile_data.py          # Data quality profiling
│   ├── init_dashboard.py        # Dashboard scaffolding
│   ├── generate_pptx_summary.py # PowerPoint generation
│   └── recalc.py                # Excel formula recalc
├── references/
│   ├── metrics.md               # SaaS metrics definitions
│   ├── biases.md                # Analytical bias checklist
│   ├── data-quality-validator.md # Data quality validation checks
│   ├── data-cleaning.md         # Cleaning patterns
│   ├── datetime-handling.md     # Date/time patterns
│   ├── dashboard-patterns.md    # Marimo dashboard patterns
│   ├── visualization-guide.md   # Chart selection guide
│   ├── report-templates.md      # Report structures
│   ├── data-wishlisting.md      # Data gap documentation
│   ├── xlsx-patterns.md         # Excel output patterns
│   └── pdf-patterns.md          # PDF handling patterns
├── SKILL.md                     # Main skill documentation
└── README.md                    # This file

License

MIT