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Quantitative Research

Checked
omer-metin·vlatest·data·Apache-2.0

Analyzes data to identify trends & insights using statistical methods, providing evidence-based conclusions for informed decision-making.

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 omer-metin/skills-for-antigravity --skill quantitative-research -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

omer-metin

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 omer-metin/skills-for-antigravity at e8dcf4e873.

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

Commit
e8dcf4e873
Manifest
skills/quantitative-research/SKILL.md
Scanned
4 files, 1,904 lines
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 enables AI agents to perform quantitative research, including analyzing data and generating insights. It can process numerical datasets, identify trends, and produce reports summarizing findings. The agent is capable of interpreting statistical information and presenting results in a clear and concise manner.

When to use it

  • Market Analysis: To analyze sales data and customer behavior to inform business decisions.
  • Scientific Research Support: To assist researchers by processing experimental data and identifying significant correlations.
  • Financial Reporting: To generate reports based on financial datasets, highlighting key performance indicators.
  • Trend Identification: To identify emerging patterns in large datasets like social media activity or website traffic.

Key capabilities

  • Data analysis
  • Statistical interpretation
  • Report generation
  • Trend identification

Example prompts

  • "Analyze this sales data and tell me the top 5 performing products."
  • "Can you identify any correlations between these two datasets?"
  • "Generate a report summarizing key financial performance indicators for Q3."

Tips & gotchas

The agent requires access to numerical datasets in a structured format (e.g., CSV, Excel) to function effectively. The quality of the results depends heavily on the accuracy and completeness of the input data.