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football-data

football-data is a skill for Claude Code, Codex from machina-sports/sports-skills. It costs 259 tokens per session (4,629 once invoked), scanned A, original, MIT.

A source of football, also called soccer, information from major leagues and competitions around the world. It covers matches, teams, players, transfers, standings, and performance measures such as expected goals (xG).

In plain words
What is it for?
Use it to get daily schedules, league standings, match statistics, player profiles, transfer information, head-to-head records, team ratings, and match forecasts.
Why use it?
It removes the need to assemble football information from separate league and statistics websites. It also provides tools for comparing teams and examining likely match outcomes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 264repo +2 changed 2d ago A scan Socket: passSnyk: warnSkillSpector: warn 259 tokens original MIT

Good fit Use it to get daily schedules, league standings, match statistics, player profiles, transfer information, head-to-head records, team ratings, and match forecasts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/machina-sports/sports-skills/football-data
View source ↗ machina-sports/sports-skills sports-skills.sh
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add machina-sports/sports-skills --skill football-data
Clone the repo
git clone --depth 1 https://github.com/machina-sports/sports-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for football-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/machina-sports/sports-skills/football-data/github.svg)](https://agentmods.dev/skills/machina-sports/sports-skills/football-data)
Your own site
<a href="https://agentmods.dev/skills/machina-sports/sports-skills/football-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/football-data/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for football-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/machina-sports/sports-skills/football-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/football-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 259 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,629 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 28 Oct 2026
  • Snyk warn 28 Oct 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 26
    YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).
    Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00259 $0.04629
Opus 5.5 $0.00104 $0.01852
Sonnet 5.5 $0.00052 $0.00926
Haiku 4.5 $0.00026 $0.00463

Measured 2d ago against content hash 3bc88b3114f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

football-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_params.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/football-data/SKILL.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Football Data

Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.

Setup

Before first use, check if the CLI is available:

which sports-skills || pip install sports-skills

If pip install fails (package not found or Python version error), install from GitHub:

pip install git+https://github.com/machina-sports/sports-skills.git

The package requires Python 3.10+. If your default Python is older, use a specific version:

python3 --version  # check version
# If < 3.10, try: python3.12 -m pip install sports-skills
# On macOS with Homebrew: /opt/homebrew/bin/python3.12 -m pip install sports-skills

No API keys required.

Quick Start

Prefer the CLI — it avoids Python import path issues:

sports-skills football get_daily_schedule
sports-skills football get_season_standings --season_id=premier-league-2025

Python SDK (alternative):

from sports_skills import football

standings = football.get_season_standings(season_id="premier-league-2025")
schedule = football.get_daily_schedule()

CRITICAL: Before Any Query

CRITICAL: Before calling any data endpoint, verify:

  • Season ID is derived from get_current_season(competition_id="...") — never hardcoded.
  • Team ID is resolved via search_team(query="...") and passed as the numeric team_id. For get_head_to_head, get_team_strength, and get_match_forecast, always pass IDs — ambiguous names (e.g. two "Paris" clubs) can resolve to the wrong team.
  • The endpoint actually covers the league in question — see the Coverage & Source Map below. Coverage is uneven across sources; an uncovered call returns an empty payload with a message, not data.
  • get_event_xg and get_event_players_statistics (with xG) are only called for top-5 leagues (EPL, La Liga, Bundesliga, Serie A, Ligue 1).
  • get_season_leaders and get_missing_players are only called for Premier League seasons (season_id must start with premier-league-).

Read the full file on GitHub · 263 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago Changed · -86 tokens per session 3bc88b3114f5
  2. 9d ago Changed · +19 lines f7b4ed1ab222
  3. 14d ago Changed · +3 tokens per session 44c0712f4cc7
  4. 1mo ago Changed · +1 lines f70339adb577
  5. 1mo ago Changed · +3 lines 5292e969168c
  6. 1mo ago Changed · +2 lines 380e4790bbaa
  7. 2mo ago Changed · +1 lines d593feca09f7
  8. 2mo ago First seen · 237 lines · 342 tokens per session scan A 56188c3c9e37

Subscribe to this mod's changes

football-data is a skill published in the GitHub repository machina-sports/sports-skills (264 stars, last pushed yesterday), licensed MIT. It adds 259 tokens to every session and 4,629 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Analytics and metricsResearch
Related

football-data compared with skills from other repositories

football-data next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
football-datamachina-sports/sports-skillsSkill264yesterdayMITA259Claude Code, Codex
imagegenopenai/codexSkill133kyesterdayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code