Skip to content
TrustedSkillsbeta
Back to all skills

Data Researcher

Listed
404kidwiz·vlatest·data

Quickly gathers, summarizes, and analyzes data from diverse online sources based on your specified research needs.

Repository

Install on your platform

This skill no longer installs under the name data-researcher. 404kidwiz/claude-supercode-skills is deleted or private, so it cannot be installed from.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

404kidwiz

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. repository not found or private Source recorded as 404kidwiz/claude-supercode-skills.
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The Data Researcher skill enables AI agents to perform in-depth data analysis and extract actionable insights from complex datasets. It specializes in integrating data from multiple sources, identifying patterns and anomalies, and transforming raw information into strategic intelligence. The agent can conduct statistical analyses, build machine learning models, and create visualizations to communicate findings effectively.

When to use it

  • Performing exploratory data analysis (EDA) on large or complicated datasets.
  • Identifying trends, correlations, and unusual occurrences within your data.
  • Combining information from different sources and formats into a unified view.
  • Creating visualizations and narratives to present data insights to stakeholders.

Key capabilities

  • Multi-Source Data Integration: Ingests data from diverse sources (databases, APIs, files, streams), harmonizes formats, and manages metadata.
  • Exploratory Data Analysis (EDA): Includes data profiling, pattern discovery, anomaly detection, and distribution analysis.
  • Statistical Analysis & Inference: Performs descriptive statistics, hypothesis testing, regression analysis, and time series analysis.
  • Machine Learning & Predictive Analytics: Implements supervised and unsupervised learning techniques, feature engineering, and model validation.
  • Advanced Data Mining: Includes association analysis, sequence mining, text mining, and graph analysis.
  • Visualization & Communication: Creates interactive visualizations for data exploration.

Example prompts

  • "Analyze sales data from the last quarter and identify any significant trends."
  • "Combine customer purchase history with website browsing behavior to find patterns."
  • "Perform anomaly detection on network traffic logs and flag suspicious activity."
  • “Create a visualization showing the correlation between advertising spend and product sales.”

Tips & gotchas

  • The skill excels with complex datasets, but may require clear instructions regarding data sources and desired analysis types.
  • Data quality is crucial; ensure your input data is reasonably clean for optimal results.