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data-journalism

Transform raw datasets into data-driven stories with Python-based workflows.

359|61|Updated Dec 25, 2025
Search Tags:#storytelling#python#visualization#data-analysis#newsroom#data-journalism
One-click install
npx skills add https://github.com/jamditis/claude-skills-journalism --skill data-journalism
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-journalism
Source: https://github.com/jamditis/claude-skills-journalism/tree/main/data-journalism
Command: npx skills add https://github.com/jamditis/claude-skills-journalism --skill data-journalism

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Journalists often struggle to turn raw datasets into actionable narratives. This Skill provides end-to-end workflows for data collection, cleaning, analysis, visualization, and storytelling to accelerate data-driven reporting.

Core Features & Use Cases

  • Data acquisition and cleaning: ingest datasets from public sources, FOIA dumps, or CSV files, then clean and normalize data for analysis.
  • Analysis and visualization: perform descriptive statistics, correlations, and produce charts/maps to support a data story.
  • Storytelling workflow: turn findings into publish-ready narratives with context, caveats, and sources for newsroom use.
  • Use Case: A newsroom receives a CSV of election results; this Skill guides you from data wrangling through visualization and a narrative outline.

Quick Start

Analyze a CSV file and generate a data-driven story with charts and a narrative outline.

Frequently Asked Questions about data-journalism

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn raw CSV data into a data-driven story?▼

It uses Python-based tooling like pandas for data wrangling and matplotlib or Plotly for charting to ensure reproducibility, while guiding the transformation of statistical findings into publish-ready narratives.

Can I use Python to analyze election results from a CSV for newsroom reporting?▼

Yes, you can analyze election results or similar public datasets by using the built-in workflows to clean and normalize the data, perform statistical analysis, and produce supporting charts.

What Python libraries are used for data visualization in this workflow?▼

This workflow uses Python-based tooling including pandas for data wrangling, and matplotlib or Plotly to generate charts and visualizations that support the narrative.

How do I clean FOIA data dumps for a data journalism investigation?▼

Yes, it guides you through data collection from public sources or FOIA dumps, cleans the raw data, and transforms the findings into publish-ready narratives with caveats and source attribution.

Do I need prior coding experience to generate charts and narratives from datasets?▼

No, it provides end-to-end workflows from data wrangling through visualization and narrative construction, making it suitable for newsroom investigations and data-driven reports across various beats.

How does reproducibility work when building data-driven reports?▼

It uses Python-based tooling like pandas for data wrangling and matplotlib or Plotly for charting to ensure reproducibility, while guiding the transformation of statistical findings into publish-ready narratives.

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