Skip to content
TrustedSkillsbeta
Back to all skills

Data Analysis

Listed
bytedance·vlatest·data·MIT

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

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add bytedance/deer-flow --skill data-analysis -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

bytedance

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. none of the 4 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as bytedance/deer-flow.
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

The data-analysis skill empowers AI agents to perform comprehensive statistical analysis, generate visualizations, and derive actionable insights from raw datasets. It supports various file formats including CSV, Excel, JSON, and SQL queries, enabling deep exploration of data structures and trends.

When to use it

  • Analyzing large-scale sales or transaction logs to identify seasonal patterns and revenue anomalies.
  • Processing user behavior metrics from web analytics tools to optimize conversion funnels.
  • Generating dynamic charts and graphs to present complex findings to non-technical stakeholders.
  • Cleaning and transforming messy raw data into structured formats ready for machine learning pipelines.

Key capabilities

  • Multi-format ingestion: Reads CSV, Excel, JSON, Parquet, and SQL database connections.
  • Statistical analysis: Calculates mean, median, standard deviation, correlation, and regression models.
  • Visualization: Generates interactive plots including histograms, scatter plots, heatmaps, and time-series graphs.
  • Data transformation: Performs filtering, grouping, aggregation, and pivoting on datasets.
  • Report generation: Outputs summary statistics and visual reports directly within the chat interface.

Example prompts

  • "Upload this CSV file of customer orders and calculate the average order value per region along with a bar chart."
  • "Run a correlation analysis between website traffic and conversion rates, then visualize the top three correlated variables."
  • "Load my SQL table sales_2023 and create a time-series line graph showing monthly revenue trends over the last year."

Tips & gotchas

Ensure your data files are properly formatted or accessible via a database connection before initiating analysis. For very large datasets, consider summarizing the data first to ensure optimal performance within the agent's processing limits.