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Agent Skill · 开发

openfootball(openfootball 命令行)

openfootball 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

openfootball 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 11,097。

总安装 1,939周安装 80GitHub Stars 4下载 634核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:openfootball(openfootball 命令行)
来源仓库:https://github.com/outsharp/shipp-skills
仓库路径:skills/openfootball
安装命令:
npx skills add https://github.com/outsharp/shipp-skills --skill openfootball
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/outsharp/shipp-skills --skill openfootball

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

openfootball 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。通过 npx skills add 命令从指定 GitHub 仓库安装使用。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/openfootball

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/outsharp/shipp-skills --skill openfootball;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

openfootball 怎么安装?

在终端执行 npx skills add https://github.com/outsharp/shipp-skills --skill openfootball。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

openfootball 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

openfootball 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

openfootball 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

openfootball 用的人多吗?

公开数据是累计安装 1,939 次,近一周安装 80 次,下载 634 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

openfootball 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
openfootball

openfootball 命令行 · 本页

1,9394接触敏感数据未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

openfootball / football.json API

openfootball is a free, open, public domain collection of football (soccer) data. The football.json repository provides pre-built JSON files for major leagues and tournaments worldwide. No API key or authentication is required.


Data Sources

There are two ways to access the data:

1. Raw GitHub URLs (Primary)

https://raw.githubusercontent.com/openfootball/football.json/master/{season}/{league}.json

2. GitHub Pages Mirror

https://openfootball.github.io/{country}/{season}/{league-name}.json
Recommendation: Use the raw GitHub URLs for the football.json repo — they use a simple, consistent naming convention and are the most reliable.

URL Structure

https://raw.githubusercontent.com/openfootball/football.json/master/{season}/{code}.json
ComponentDescriptionExamples
{season}Season directory — cross-year or calendar year2024-25, 2023-24, 2025, 2019
{code}League code in {country}.{division} formaten.1, de.1, es.1, it.1, fr.1

Available Leagues

England

CodeLeagueTier
en.1English Premier League1st division
en.2English Championship2nd division
en.3English League One3rd division
en.4English League Two4th division

Germany

CodeLeagueTier
de.1Deutsche Bundesliga1st division
de.22. Bundesliga2nd division
de.33. Liga3rd division

Spain

CodeLeagueTier
es.1Primera División (La Liga)1st division
es.2Segunda División2nd division

Italy

CodeLeagueTier
it.1Serie A1st division
it.2Serie B2nd division

France

CodeLeagueTier
fr.1Ligue 11st division
fr.2Ligue 22nd division
Note: Not all leagues are available for all seasons. The football.json repo is continuously updated — check the repository for the full list of available files.

Available Seasons

Season directories in the football.json repo go back to 2010-11. European leagues use cross-year format (2024-25), while some calendar-year leagues use single-year format (2025).

FormatUsageExamples
YYYY-YYEuropean club seasons (Aug–May)2024-25, 2023-24, 2015-16
YYYYCalendar-year competitions2025, 2020, 2019

Known season directories: 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, 2017-18, 2018-19, 2019-20, 2020-21, 2021-22, 2022-23, 2023-24, 2024-25, 2025-26, 2019, 2020, 2025.


JSON Response Format

All files follow the same JSON schema:

{
  "name": "English Premier League 2024/25",
  "matches": [
    {
      "round": "Matchday 1",
      "date": "2024-08-16",
      "time": "20:00",
      "team1": "Manchester United FC",
      "team2": "Fulham FC",
      "score": {
        "ht": [0, 0],
        "ft": [1, 0]
      }
    }
  ]
}

Top-Level Fields

FieldTypeDescription
namestringHuman-readable league name and season
matchesarrayArray of match objects

Match Object Fields

FieldTypeRequiredDescription
roundstringYesRound/matchday name (e.g., "Matchday 1", "Round of 16")
datestringYesMatch date in YYYY-MM-DD format
timestringNoKick-off time in HH:MM format (24-hour, local time)
team1stringYesHome team name
team2stringYesAway team name
scoreobjectNoScore object (absent for unplayed future matches)
statusstringNoSpecial status (e.g., "awarded" for administratively decided results)

Score Object Fields

FieldTypeDescription
ft[int, int]Full-time score [home, away]
ht[int, int]Half-time score [home, away] (may be absent for some matches)
Note: Some matches only have ft (full-time) without ht (half-time). Always check for the presence of ht before accessing it.

Common Patterns

Fetch a League Season (curl)

curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" | jq .

Fetch and Parse Match Data (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

print(f"League: {data['name']}")
print(f"Total matches: {len(data['matches'])}")

for match in data["matches"][:10]:
    ft = match.get("score", {}).get("ft")
    if ft:
        print(f"  {match['date']}  {match['team1']} {ft[0]}-{ft[1]} {match['team2']}")
    else:
        print(f"  {match['date']}  {match['team1']} vs {match['team2']} (no score)")

Build a League Table from Results (Python)

import requests
from collections import defaultdict

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

table = defaultdict(lambda: {"played": 0, "won": 0, "drawn": 0, "lost": 0,
                              "gf": 0, "ga": 0, "points": 0})

for match in data["matches"]:
    score = match.get("score", {}).get("ft")
    if not score:
        continue

    t1, t2 = match["team1"], match["team2"]
    g1, g2 = score

    for team, gf, ga in [(t1, g1, g2), (t2, g2, g1)]:
        table[team]["played"] += 1
        table[team]["gf"] += gf
        table[team]["ga"] += ga
        if gf > ga:
            table[team]["won"] += 1
            table[team]["points"] += 3
        elif gf == ga:
            table[team]["drawn"] += 1
            table[team]["points"] += 1
        else:
            table[team]["lost"] += 1

# Sort by points, then goal difference
sorted_table = sorted(table.items(),
                       key=lambda x: (x[1]["points"], x[1]["gf"] - x[1]["ga"]),
                       reverse=True)

print(f"{'Team':<35} {'P':>3} {'W':>3} {'D':>3} {'L':>3} {'GF':>4} {'GA':>4} {'GD':>4} {'Pts':>4}")
print("-" * 70)
for i, (team, stats) in enumerate(sorted_table, 1):
    gd = stats["gf"] - stats["ga"]
    print(f"{i:>2}. {team:<32} {stats['played']:>3} {stats['won']:>3} "
          f"{stats['drawn']:>3} {stats['lost']:>3} {stats['gf']:>4} "
          f"{stats['ga']:>4} {gd:>+4} {stats['points']:>4}")

Filter Matches by Team (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

team = "Arsenal FC"
matches = [m for m in data["matches"]
           if team in (m["team1"], m["team2"]) and m.get("score", {}).get("ft")]

for m in matches:
    ft = m["score"]["ft"]
    opponent = m["team2"] if m["team1"] == team else m["team1"]
    venue = "H" if m["team1"] == team else "A"
    my_goals = ft[0] if m["team1"] == team else ft[1]
    opp_goals = ft[1] if m["team1"] == team else ft[0]
    result = "W" if my_goals > opp_goals else ("D" if my_goals == opp_goals else "L")
    print(f"  {m['date']} ({venue}) {result} {my_goals}-{opp_goals} vs {opponent}")

Fetch Multiple Leagues (Python)

import requests

leagues = {
    "Premier League": "en.1",
    "Bundesliga": "de.1",
    "La Liga": "es.1",
    "Serie A": "it.1",
    "Ligue 1": "fr.1",
}

season = "2024-25"
base = "https://raw.githubusercontent.com/openfootball/football.json/master"

for name, code in leagues.items():
    url = f"{base}/{season}/{code}.json"
    resp = requests.get(url)
    if resp.status_code == 200:
        data = resp.json()
        total = len(data["matches"])
        played = sum(1 for m in data["matches"] if m.get("score", {}).get("ft"))
        print(f"{name}: {played}/{total} matches played")
    else:
        print(f"{name}: not available for {season}")

Fetch and Parse (Node.js)

const url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json";

const res = await fetch(url);
const data = await res.json();

console.log(`League: ${data.name}`);
console.log(`Matches: ${data.matches.length}`);

data.matches.slice(0, 10).forEach((m) => {
  const ft = m.score?.ft;
  if (ft) {
    console.log(`  ${m.date}  ${m.team1} ${ft[0]}-${ft[1]} ${m.team2}`);
  }
});

Fetch and Parse (bash + jq)

# Get all results for a specific team
curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" \
  | jq -r '.matches[]
    | select(.team1 == "Liverpool FC" or .team2 == "Liverpool FC")
    | select(.score.ft)
    | "\(.date) \(.team1) \(.score.ft[0])-\(.score.ft[1]) \(.team2)"'

Compare Head-to-Head Results (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

team_a = "Arsenal FC"
team_b = "Liverpool FC"

h2h = [m for m in data["matches"]
       if {m["team1"], m["team2"]} == {team_a, team_b}
       and m.get("score", {}).get("ft")]

for m in h2h:
    ft = m["score"]["ft"]
    ht = m["score"].get("ht", ["?", "?"])
    print(f"{m['date']}: {m['team1']} {ft[0]}-{ft[1]} {m['team2']} (HT: {ht[0]}-{ht[1]})")

Aggregate Stats Across Seasons (Python)

import requests

base = "https://raw.githubusercontent.com/openfootball/football.json/master"
seasons = ["2022-23", "2023-24", "2024-25"]
team = "Manchester City FC"
all_results = {"W": 0, "D": 0, "L": 0, "GF": 0, "GA": 0}

for season in seasons:
    resp = requests.get(f"{base}/{season}/en.1.json")
    if resp.status_code != 200:
        continue
    data = resp.json()
    for m in data["matches"]:
        ft = m.get("score", {}).get("ft")
        if not ft:
            continue
        if m["team1"] == team:
            gf, ga = ft
        elif m["team2"] == team:
            ga, gf = ft
        else:
            continue
        all_results["GF"] += gf
        all_results["GA"] += ga
        if gf > ga:
            all_results["W"] += 1
        elif gf == ga:
            all_results["D"] += 1
        else:
            all_results["L"] += 1

print(f"{team} across {', '.join(seasons)}:")
print(f"  W{all_results['W']} D{all_results['D']} L{all_results['L']}")
print(f"  Goals: {all_results['GF']} scored, {all_results['GA']} conceded")

Find High-Scoring Matches (bash + jq)

# Find all matches with 5+ total goals in the Premier League 2024/25
curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" \
  | jq -r '.matches[]
    | select(.score.ft)
    | select((.score.ft[0] + .score.ft[1]) >= 5)
    | "\(.date) \(.team1) \(.score.ft[0])-\(.score.ft[1]) \(.team2) (Total: \(.score.ft[0] + .score.ft[1]))"'

Get Results for a Specific Matchday (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

matchday = "Matchday 38"
matches = [m for m in data["matches"] if m["round"] == matchday]

print(f"--- {matchday} ---")
for m in matches:
    ft = m.get("score", {}).get("ft")
    if ft:
        print(f"  {m['date']} {m.get('time', '')}  {m['team1']} {ft[0]}-{ft[1]} {m['team2']}")
    else:
        print(f"  {m['date']} {m.get('time', '')}  {m['team1']} vs {m['team2']}")

Other Repositories (Source Data)

The football.json files are auto-generated from plain-text Football.TXT source files in country-specific repos:

RepoContentGitHub URL
englandEPL, Championship, League One, League Twoopenfootball/england
deutschlandBundesliga, 2. Bundesliga, 3. Liga, DFB Pokalopenfootball/deutschland
espanaLa Liga, Segunda Divisiónopenfootball/espana
italySerie A, Serie B, Coppa Italiaopenfootball/italy
franceLigue 1, Ligue 2openfootball/europe (in /europe)
worldcupFIFA World Cup (2022, 2018, 2014, etc.)openfootball/worldcup
euroEuro 2024, 2020, 2016, etc.openfootball/euro
champions-leagueUCL & Europa Leagueopenfootball/champions-league
clubsClub & stadium metadataopenfootball/clubs
worldLeagues from N. America, Asia, Africa, Australiaopenfootball/world

Country repos also have their own GitHub Pages JSON mirrors. For example:

https://openfootball.github.io/england/2024-25/1-premierleague.json

You can convert Football.TXT source files to JSON yourself using the fbtxt2json CLI tool:

# Convert a single league file
fbtxt2json england/2025-26/1-premierleague.txt -o en.1.json

# Convert an entire country repo at once
fbtxt2json . -o ./_site

Rate Limits

GitHub does not publish specific rate limits for raw content, but general guidelines:

GuidelineRecommendation
Polling interval≥ 60 seconds between requests for the same file
Concurrent requestsKeep reasonable (< 20 concurrent)
CachingCache responses locally — data changes infrequently
Unauthenticated GitHub API60 requests/hour per IP (only applies to API endpoints, not raw content)
Tip: Since match data doesn't change after a game is completed, you can aggressively cache historical seasons. Only poll the current season for updates.

Error Handling

HTTP StatusMeaningAction
200SuccessParse the JSON
404File not foundCheck the season, league code, or URL spelling
429Rate limitedBack off and retry after a delay
5xxServer errorRetry with exponential backoff

Tips

  • No auth needed — all data is fully public. Start fetching immediately.
  • Check for score before accessing — future/unplayed matches won't have a score field.
  • Check for ht separately — some matches have ft but no ht data.
  • Team names include suffixes — e.g., "Arsenal FC", "Manchester United FC", "Borussia Dortmund". Use exact string matching.
  • Team names vary by league — German teams use German names ("FC Bayern München"), English teams use English names ("Arsenal FC").
  • The status field is rare — it appears on administratively decided matches (e.g., "awarded").
  • Cache aggressively — completed seasons never change. Only the current season gets updates.
  • Build tables from the data — the dataset provides raw match results; you compute standings, form, H2H, etc.
  • Cross-reference leagues — fetch multiple league files to compare across countries.
  • Public domain (CC0) — use the data however you want with no restrictions whatsoever.
  • Don't edit the JSON directly — if contributing, edit the Football.TXT source files in the country repos; JSON is auto-generated.
  • Time field is local — the time value represents the local kick-off time for the match venue.
  • Scores are [home, away] — score.ft[0] is always the team1 (home) goals, score.ft[1] is always the team2 (away) goals.

Changelog

  • 0.1.0 — Initial release with league data access, JSON schema documentation, and common usage patterns.

平台分布

Codex

36.37%
按下载量换算231

Claude

30.37%
按下载量换算193

Cursor

17.89%
按下载量换算113

Gemini CLI

9.42%
按下载量换算60

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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