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 machina-sports/sports-skills --skill fastf1git clone --depth 1 https://github.com/machina-sports/sports-skillsWrote 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/machina-sports/sports-skills/fastf1)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/fastf1"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/fastf1/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/machina-sports/sports-skills/fastf1"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/fastf1.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00137 | $0.01721 |
| Opus 5 | $0.00068 | $0.00860 |
| Sonnet 5 | $0.00027 | $0.00344 |
| Haiku 4.5 | $0.00014 | $0.00172 |
Grade A, and why
fastf1 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 12d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FastF1 — Formula 1 Data
Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.
Quick Start
Prefer the CLI — it avoids Python import path issues:
sports-skills f1 get_race_schedule --year=2025
sports-skills f1 get_race_results --year=2025 --event=Monza
Python SDK (alternative):
from sports_skills import f1
schedule = f1.get_race_schedule(year=2025)
results = f1.get_race_results(year=2025, event="Monza")
CRITICAL: Before Any Query
CRITICAL: Before calling any data endpoint, verify:
- Year is derived from the system prompt's
currentDate— never hardcoded. - In January or February, use
year = current_year - 1(pre-season; the new F1 season has not started yet).
Choosing the Year
Derive the current year from the system prompt's date (e.g., currentDate: 2026-02-16 → current year is 2026).
- If the user specifies a year, use it as-is.
- If the user says "latest", "recent", "last season", or doesn't specify: The F1 season runs roughly March–December. If the current month is January or February, use
year = current_year - 1. From March onward, use the current year.
Workflows
Race Weekend Analysis
get_race_schedule --year=<year>— find the event name and dateget_race_results --year=<year> --event=<name>— final classification (positions, times, points)get_lap_data --year=<year> --event=<name> --session_type=R— lap-by-lap pace analysisget_tire_analysis --year=<year> --event=<name>— strategy breakdown (compounds, stint lengths, degradation)
Driver/Team Comparison
get_championship_standings --year=<year>— championship context (points, wins, podiums)get_team_comparison --year=<year> --team1=<t1> --team2=<t2>ORget_driver_comparison --year=<year> --driver1=<d1> --driver2=<d2>get_season_stats --year=<year>— aggregate performance (fastest laps, top speeds)
Season Overview
get_race_schedule --year=<year>— full calendar with dates and circuitsget_championship_standings --year=<year>— driver and constructor standingsget_season_stats --year=<year>— season-wide fastest laps, top speeds, points leadersget_driver_info --year=<year>— current grid (driver numbers, teams, nationalities)
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.
- 12d ago First seen · 138 lines · 137 tokens per session scan A d1f39ceb2142
fastf1 is a skill published in the GitHub repository machina-sports/sports-skills (218 stars, last pushed 4d ago), licensed MIT. It adds 137 tokens to every session and 1,721 once invoked, about $0.0007 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…