Data Analysis

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by scientiacapital · vlatest · Repository

Helps with data, analysis as part of data analysis and analytics workflows workflows.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add scientiacapital-data-analysis npx -- -y @trustedskills/scientiacapital-data-analysis
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "scientiacapital-data-analysis": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/scientiacapital-data-analysis"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

What it does

This skill provides executive-grade data analysis capabilities, specifically designed for creating presentations and reports suitable for venture capital, private equity, and C-suite audiences. It leverages tools like pandas, polars, Plotly, Altair, and Streamlit to load, clean, analyze, visualize, and present data effectively. The skill supports loading data from a variety of formats (CSV, Excel, JSON, Parquet, PDF, PPTX) and calculating key SaaS metrics such as MRR, ARR, LTV, CAC, and churn.

When to use it

  • Generating investor-ready presentations with clear, concise charts and dashboards.
  • Analyzing SaaS business performance using standard metrics like MRR, ARR, and churn.
  • Extracting data from PDF or PowerPoint documents for further analysis.
  • Creating interactive Streamlit dashboards to explore and present key findings.
  • Performing cohort retention analysis to understand customer behavior over time.

Key capabilities

  • Universal Data Loader: Loads data from CSV, Excel, JSON, Parquet, PDF, and PPTX files.
  • Data Wrangling: Cleans datasets using pandas/polars transforms (dropna, dedup, type conversion).
  • SaaS Metrics Calculation: Calculates key SaaS metrics including MRR, ARR, LTV, CAC, and churn.
  • Cohort Retention Analysis: Performs retention analysis to understand customer behavior over time.
  • McKinsey-style Charting: Creates charts with action titles, high data-ink ratio (>80%), and one message per chart using executive colors (#003366, #2E7D32, #C62828).
  • Streamlit Dashboard Creation: Builds interactive dashboards for data exploration.

Example prompts

  • "Load the data from 'sales_data.csv' and calculate MRR."
  • "Analyze customer retention using cohort analysis on the provided Excel file."
  • "Create a McKinsey-style chart showing Q4 revenue performance, highlighting if it exceeded target."
  • “Generate a Streamlit dashboard displaying key SaaS metrics.”

Tips & gotchas

  • The skill uses pandas and polars for data manipulation. Familiarity with these libraries can be helpful.
  • Charts are designed to follow McKinsey principles; ensure your data supports clear, concise messaging.
  • For narrative generation alongside the analysis, Claude is recommended over OpenAI.

Tags

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Details

Version
vlatest
License
Author
scientiacapital
Installs
36

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Passed automated security scans.