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.
npx skills add ricardoherediaj/football-analytics-tutorials --skill football-match-reportgit clone --depth 1 https://github.com/ricardoherediaj/football-analytics-tutorialsWrote 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.
[](https://agentmods.dev/skills/ricardoherediaj/football-analytics-tutorials/football-match-report)<a href="https://agentmods.dev/skills/ricardoherediaj/football-analytics-tutorials/football-match-report"><img src="https://agentmods.dev/badge/skills/ricardoherediaj/football-analytics-tutorials/football-match-report/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.
<a href="https://agentmods.dev/skills/ricardoherediaj/football-analytics-tutorials/football-match-report"><img src="https://agentmods.dev/badge/skills/ricardoherediaj/football-analytics-tutorials/football-match-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.02805 |
| Opus 5.5 | $0.00012 | $0.01122 |
| Sonnet 5.5 | $0.00006 | $0.00561 |
| Haiku 4.5 | $0.00003 | $0.00281 |
Grade A, and why
football-match-report 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 2mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Football Match Report Skill
Turns any WhoScored match URL into a two-page post-match team report by merging two data sources:
- WhoScored (headless Chromium) → full event stream (passes, tackles, carries, recoveries). Powers passing networks, defensive blocks, progressive passes/carries, xT momentum and all zone charts.
- FotMob (plain requests, no token) → shots with xG/xGOT, native match momentum, official stats, player of the match, and real team colors.
The FotMob match id is auto-resolved from the WhoScored match date + team
names — a single WhoScored URL is all you need. Both sources are required
for the complete report; a FotMob-only scrape (--fotmob-id) renders a
reduced report (shots/momentum/stats) without the event panels.
Charts follow the Post-Match-Report-2.0 blueprint (Adnan Ahmed): UEFA pitch, black background, shirt numbers inside player nodes (circle = starter, box = sub), line-height markers, xT momentum.
Two commands, end to end:
python scripts/scrape_match.py "<whoscored-url>" --out ./data
python scripts/render_report.py --data ./data --out ./report
A third command adds the player-level reports (Top Players dashboard, per-player dashboards, per-player stats CSV):
uv run football-match-report players --data ./data --out ./report
When to Use
- A user asks for a post-match report, match dashboard, or football analytics breakdown and provides a WhoScored match URL (or match id).
- A recurring match-report job (e.g. after each round of fixtures).
- Rebuilding/updating an old notebook-based report into the modern pipeline.
Don't use for: season-long datasets (use soccerdata/StatsBomb open data),
live in-play streams, or non-WhoScored competitions (Understat etc.).
Prerequisites
- Python 3.10+ with
uv(or pip). - Install deps:
uv sync --extra scrape(orpip install -e ".[scrape]"). - One-time browser install:
uv run playwright install chromium(WhoScored is bot-walled; the script launches headless Chromium to read the embedded matchCentreData JSON). - No API keys. FotMob's public endpoint (
/api/data/matchDetails) is used for shots/xG/momentum/colors and does not require a token.
What ships with it
4 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.
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.
- 2mo ago First seen · 234 lines · 30 tokens per session scan A 76a00c1064ac
football-match-report is a skill published in the GitHub repository ricardoherediaj/football-analytics-tutorials (119 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 2,805 once invoked, about $0.0001 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.
football-match-report compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| football-match-reportricardoherediaj/football-analytics-tutorials | Skill | 119 | 3mo ago | Apache-2.0 | A | 30 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |