OpenAgentSkill
GitHub200+

Skill rankings

Best data analysis skills for AI agents

Discover skills for inspecting CSVs, summarizing datasets, creating charts, validating metrics, and helping agents reason over structured data.

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Shown: 4 · Candidates: 62

Compare top 4

Saved directory data is shown because current data is unavailable. Dates and metrics may be out of date.

  1. 01

    StatsBomb Open Data

    Open football event data for match analysis, xG research, scouting, and World Cup-style dashboards.

    @statsbombSports Analytics3,900
    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
    72/100
    Quality
    100/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    77/100
    Task fit
    14/100
  2. 02

    mplsoccer

    Football pitch plotting and soccer analytics visuals for match reports and dashboards.

    @andrewRowlinsonSports Analytics1,100
    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
    61/100
    Quality
    97/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    77/100
    Task fit
    16/100
  3. 03

    RNSkill Content Retrospective

    Turn creator performance data into a documented retrospective, grounded hypotheses, and reusable content learnings.

    @PluviobyteVideo Creation797
    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
    58/100
    Quality
    100/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    90/100
    Task fit
    13/100
  4. 04

    Portfolio Analytics

    Measure portfolio returns, risk, drawdowns, benchmarks, rolling metrics, and strategy performance from approved data.

    @agiprolabsFinance267
    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
    49/100
    Quality
    100/100
    Freshness
    0/100
    Agent evidence
    0/100
    Evidence confidence
    0/100
    Install readiness
    90/100
    Task fit
    12/100

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.