A Formula 1 data tool built around the FastF1 library. It provides race schedules, results, session information, lap times, driver and team details, sector times, and tyre strategy.

Use it to find the F1 calendar, race and qualifying results, practice sessions, lap times, sector times, driver or team information, and tyre strategies.

It removes the need to collect timing and race information manually from different Formula 1 sources. It organizes both event-level results and detailed lap data.

fastf1 — Skill for Claude Code and Codex by machina-sports

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 fastf1

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/machina-sports/sports-skills/fastf1/github.svg)](https://agentmods.dev/skills/machina-sports/sports-skills/fastf1)

Your own site

<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.

Your own site · 80×15

<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>

Per session 137 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,995 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 1 Oct 2026
Snyk pass 1 Oct 2026
NVIDIA SkillSpector pass 7 Sept 2026

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.

Model	Per session	Once invoked
Fable 5.1	$0.00137	$0.01995
Opus 5.5	$0.00055	$0.00798
Sonnet 5.5	$0.00027	$0.00399
Haiku 4.5	$0.00014	$0.00199

Measured 9d ago against content hash 4ea811ede76c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 9d 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.

skills/fastf1/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 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 date
get_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 analysis
get_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> OR get_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 circuits
get_championship_standings --year=<year> — driver and constructor standings
get_season_stats --year=<year> — season-wide fastest laps, top speeds, points leaders
get_driver_info --year=<year> — current grid (driver numbers, teams, nationalities)

Read the full file on GitHub · 157 lines

Files

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.

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.

9d ago Changed · +19 lines 4ea811ede76c
1mo ago Changed 59ad31427310
2mo ago First seen · 138 lines · 137 tokens per session scan A d1f39ceb2142

Subscribe to this mod's changes

fastf1 is a skill published in the GitHub repository machina-sports/sports-skills (264 stars, last pushed yesterday), licensed MIT. It adds 137 tokens to every session and 1,995 once invoked, about $0.0005 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 metrics

fastf1 next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
fastf1machina-sports/sports-skills	Skill	264	yesterday	MIT	A	137	Claude Code, Codex
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code