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Wnba Data

Checked
machina-sports·vlatest·data·MIT

Access comprehensive WNBA player stats, team records, schedules, and historical data powered by machina-sports' AI.

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 wnba-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/wnba-data/SKILL.md
Scanned
4 files, 320 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 wnba-data skill provides AI agents with access to official WNBA statistics, game results, and player performance metrics. It enables automated analysis of team standings, individual player stats, and historical game data without manual lookup.

When to use it

  • Analyze current season trends for specific teams or players.
  • Generate comparative reports on offensive and defensive efficiency.
  • Track player statistics over a defined timeframe or across multiple seasons.
  • Retrieve real-time or recent game summaries and final scores.

Key capabilities

  • Access to official WNBA statistical datasets.
  • Retrieval of game results and box scores.
  • Support for querying specific player performance metrics.
  • Data aggregation for team standings and league-wide trends.

Example prompts

  • "Compare the average points per game for top scorers in the 2023 season."
  • "What was the final score of the last matchup between the Las Vegas Aces and the New York Liberty?"
  • "List the defensive rebounding averages for all players on the Connecticut Sun roster this year."

Tips & gotchas

Ensure your queries specify clear timeframes or seasons, as historical data availability may vary. For complex multi-player comparisons, break requests into smaller batches to maintain accuracy.