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Data Analysis

Listed
casper-studios·vlatest·data·MPL-2.0

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 casper-studios/casper-marketplace --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

casper-studios

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.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: openoffice.org.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    github.comsource hostopenoffice.orgundeclared

    What it found

    • plugins/product/data-analysis/scripts/recalc.py:72contacts openoffice.org
      <script:module xmlns:script="http://openoffice.org/2000/script" script:name="Module1" script:language="StarBasic">

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned casper-studios/casper-marketplace at 99ee1b05bb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
99ee1b05bb
Manifest
plugins/product/data-analysis/SKILL.md
Scanned
22 files, 7,491 lines
Copies in repo
2 — all of them scanned
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

This skill provides a structured workflow for data analysis and storytelling, particularly suited for financial, SaaS, and RevOps contexts. It transforms raw data into actionable insights while maintaining transparency through logged analytical decisions and bias awareness. The process involves seven phases, from initial notebook setup to final report generation, ensuring reproducibility and progressive disclosure of findings.

When to use it

  • Analyzing financial performance metrics to identify trends and areas for improvement.
  • Creating reports on SaaS usage data to understand customer behavior and churn drivers.
  • Developing RevOps dashboards that track key operational efficiency indicators.
  • Building presentations or reports that require clear explanations of analytical choices.
  • When needing a documented audit trail of all analytical decisions made during the process.

Key capabilities

  • Structured 7-phase workflow: Setup, Ingest, Explore, Model (optional), Interpret, Wishlist, and Output.
  • Decision Logging Protocol: Requires logging every analytical choice with specific formats for filtering, metric selection, visualizations, assumptions, and proxy usage.
  • Marimo Notebook Initialization: Provides a script (init_marimo_notebook.py) to create pre-built notebooks.
  • Bias Awareness & Progressive Disclosure: Encourages consideration of biases and phased reporting of insights.

Example prompts

  • "Create a data analysis notebook for our Q3 SaaS revenue, following the standard workflow."
  • "Analyze customer churn data and document all decisions made in the decision log format."
  • "Generate a report on marketing campaign performance, including an assessment of any assumptions used."

Tips & gotchas

  • Notebook Initialization Required: You must run python scripts/init_marimo_notebook.py <notebook_name> to set up a new analysis project.
  • Decision Logging is Mandatory: The skill requires meticulous logging of all analytical choices for reproducibility and transparency. Failure to log decisions will impact the quality and usefulness of the analysis.