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Cbb Data

Checked
machina-sports·vlatest·other·MIT

Access comprehensive college basketball data—scores, stats, schedules, recruiting—directly from machina-sports' cbb-data skill.

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 machina-sports/sports-skills --skill cbb-data -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

machina-sports

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 machina-sports/sports-skills at 3be3624236.

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

Commit
3be3624236
Manifest
skills/cbb-data/SKILL.md
Scanned
5 files, 437 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

The cbb-data skill provides AI agents with access to comprehensive College Basketball data, enabling real-time analysis of game statistics and player performance. It supports queries across teams, players, and historical records within the NCAA landscape.

When to use it

  • Retrieving live box scores and play-by-play data for current CBB games.
  • Analyzing historical team trends or individual player career statistics.
  • Generating comparative reports between specific conferences or divisions.
  • Accessing roster information and schedule details for upcoming seasons.

Key capabilities

  • Real-time game statistics retrieval.
  • Historical performance data access.
  • Team and player profile lookups.
  • Conference-specific data filtering.

Example prompts

  • "What were the final box scores and key stats from last night's ACC tournament games?"
  • "Compare the career points per game averages of top scorers in the Big Ten over the last three seasons."
  • "List the current rosters and upcoming schedules for all teams in the Pac-12 conference."

Tips & gotchas

Ensure your AI agent has active internet connectivity to fetch the most up-to-date live scores, as historical data may be cached depending on the implementation. Verify that the specific dataset covers the exact season or timeframe you require before running complex analytical queries.