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Cfb Data

Checked
machina-sports·vlatest·agents·MIT

Access comprehensive college football data—stats, schedules, historical results—directly through this Machina Sports AI agent 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 cfb-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/cfb-data/SKILL.md
Scanned
5 files, 502 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 cfb-data skill provides AI agents with access to comprehensive College Football data, enabling real-time analysis of team statistics, player performance, and game outcomes. It serves as a foundational tool for sports analytics applications requiring deep insights into the NCAA football landscape.

When to use it

  • Analyzing historical trends in conference standings or divisional matchups over multiple seasons.
  • Generating pre-game reports that compare offensive and defensive metrics between two specific teams.
  • Tracking individual player statistics such as rushing yards, passing touchdowns, or interception rates during a season.
  • Creating dynamic dashboards for fantasy football leagues based on current team performance data.

Key capabilities

  • Retrieves detailed team stats including win-loss records, point differentials, and ranking positions.
  • Accesses player-specific data covering offensive and defensive contributions across various positions.
  • Supports queries for game-by-game results, scores, and key event summaries from recent matches.
  • Facilitates comparisons between teams or players using standardized statistical categories.

Example prompts

  • "What are the top 10 rushing leaders in the Big Ten this season?"
  • "Compare the defensive efficiency of Alabama and Georgia for the last five games."
  • "Provide a summary of all bowl game results from the previous weekend."

Tips & gotchas

Ensure your AI agent has proper authentication or API access configured to retrieve real-time data, as some endpoints may require rate limiting adherence. This skill is best used in conjunction with visualization tools to present complex datasets clearly to end users.