OpenAgentSkill
GitHub200+

Skill rankings

Best sports analytics skills for AI agents

Discover skills for football data, World Cup analysis, match prediction, xG, player scouting, video tracking, dashboards, and sports research agents.

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Shown: 3 · Candidates: 471

Compare top 4
  1. 01

    Last30days Skill

    Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.

    @mvanhornResearch63,666
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    Ranking signals

    Signals use a 0–100 scale. They explain the shortlist; the ordering follows this list’s method, not any one score.

    Popularity
    96/100
    Quality
    100/100
    Freshness
    92/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    100/100
    Task fit
    19/100
  2. 02

    Apple Design

    Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

    @emilkowalskiDesign34,452
    Review before use
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    Ranking signals

    Signals use a 0–100 scale. They explain the shortlist; the ordering follows this list’s method, not any one score.

    Popularity
    91/100
    Quality
    100/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    100/100
    Task fit
    18/100
  3. 03

    Cli

    Official Lark/Feishu CLI tool with 200+ commands and 26 AI agent skills, designed for agent-native operation and easy integration with AI runtimes.

    @larksuiteProductivity16,552
    Review before use
    View details
    Ranking signals

    Signals use a 0–100 scale. They explain the shortlist; the ordering follows this list’s method, not any one score.

    Popularity
    84/100
    Quality
    100/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    100/100
    Task fit
    17/100

A shortlist from up to 480 directory candidates, not the entire registry. Stars belong to repositories. Signals are not safety guarantees or runtime verification.

How this list works

Matches task and source metadata, then considers quality, popularity and freshness. Source descriptions are not execution evidence.

Signals use a 0–100 scale. They explain the shortlist; the ordering follows this list’s method, not any one score.