Agent skill

Fastf1

by machina-sports in machina-sports/sports-skills

Formula 1 data — race schedules, results, lap timing, driver and team info.

Install Fastf1

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fastf1 .claude/skills/fastf1 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .claude/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1 --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/fastf1 .agents/skills/fastf1 && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .agents/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1 --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/fastf1 .cursor/skills/fastf1 && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .cursor/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/machina-sports/sports-skills.git --path skills/fastf1

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1 --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/fastf1 .gemini/skills/fastf1 && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .gemini/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/fastf1 .github/skills/fastf1 && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .github/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add machina-sports/sports-skills --skill fastf1 -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install machina-sports/sports-skills fastf1 --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/machina-sports/sports-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/fastf1 .opencode/skills/fastf1 && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "fastf1" agent skill from https://github.com/machina-sports/sports-skills/tree/main/skills/fastf1 into .opencode/skills/fastf1/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fastf1", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: fastf1
GitHub stars: 245
Token cost: ~1.6k tokens
SKILL.md length: 678 words
Files: 5 (incl. scripts, references)
Skills in repo: 23
Repo updated: Oct 10, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Formula 1 data — race schedules, results, lap timing, driver and team info.

Works in 4 steps: get_race_schedule --year= — find the… → get_race_results --year= --event= —… → get_lap_data --year= --event=… → …
: user asks about F1 race results
SKILL.md covers Quick Start, CRITICAL: Before Any Query, Choosing the Year and Workflows, plus 4 more sections
Runs Shell scripts from its folder

What it does

Fastf1 is an agent skill from machina-sports/sports-skills. Formula 1 data — race schedules, results, lap timing, driver and team info. Powered by the FastF1 library. Covers F1 sessions, qualifying, practice, race results, sector times, tire strategy. Use when: user asks about F1 race results, qualifying, lap times, driver stats, team info, the F1 calendar, or Formula 1 data. Don't use when: user asks about other motorsports (MotoGP, NASCAR, IndyCar, WEC, Formula E). Don't use for F1 betting odds or predictions — use kalshi or polymarket instead. Don't use for F1 news…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/commands.md` and `references/schemas.md`). Compatibility notes: Requires Python 3.10+ (install with: pip install sports-skills)

It sits in Data & Analytics. It works with Kalshi and Polymarket. The repository describes itself as: Open-source agent skills for live sports data and prediction markets. Football, F1, Kalshi, Polymarket. Zero API keys. SKILL.md format. The licence is MIT.

When your agent uses it

: user asks about F1 race results
The F1 calendar
: user asks about other motorsports (MotoGP
F1 betting odds

Example prompts

“/fastf1”

Requirements

Python 3
A Bash shell
Compatibility (from SKILL.md): Requires Python 3.10+ (install with: pip install sports-skills)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

get_race_schedule --year= — find the event name and date
get_race_results --year= --event= — final classification (positions, times, points)
get_lap_data --year= --event= --session_type=R — lap-by-lap pace analysis
get_tire_analysis --year= --event= — strategy breakdown (compounds, stint lengths, degradation)

What it can do on your machine

Read from SKILL.md and the folder at commit 0420a7c. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Requires Python 3.10+ (install with: pip install sports-skills)
From compatibility in the SKILL.md frontmatter.

Context cost

Fastf1 loads about 1.6k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 140 tokens; SKILL.md has 678 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~140
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from machina-sports/sports-skills at commit 0420a7c, republished under its MIT licence (© machina-sports). 678 words, ~1,645 tokens.

Download SKILL.mdSave it as .claude/skills/fastf1/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.

name: fastf1
description: Formula 1 data — race schedules, results, lap timing, driver and team info. Powered by the FastF1 library. Covers F1 sessions, qualifying, practice, race results, sector times, tire strategy. Use when: user asks about F1 race results, qualifying, lap times, driver stats, team info, the F1 calendar, or Formula 1 data. Don't use when: user asks about other motorsports (MotoGP, NASCAR, IndyCar, WEC, Formula E). Don't use for F1 betting odds or predictions — use kalshi or polymarket instead. Don't use for F1 news articles — use sports-news instead.
compatibility: Requires Python 3.10+ (install with: pip install sports-skills)
license: MIT
metadata.author: machina-sports
metadata.version: 0.1.0

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:

bash

sports-skills f1 get_race_schedule --year=2025
sports-skills f1 get_race_results --year=2025 --event=Monza

Python SDK (alternative):

python

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)

Commands

Command	Description
`get_race_schedule`	Full season calendar with dates and circuits
`get_race_results`	Final race classification (positions, times, points)
`get_session_data`	Raw session info (Q, FP1, FP2, FP3, R)
`get_driver_info`	Driver details from the grid
`get_team_info`	Team info with driver lineup
`get_lap_data`	Lap-by-lap timing with sectors and tire data
`get_pit_stops`	Pit stops with pit-lane time (not stationary time) and team averages
`get_speed_data`	Speed trap and intermediate speed data
`get_championship_standings`	Driver and constructor championship standings (`--round=N` for standings after round N)
`get_season_stats`	Aggregate season performance
`get_team_comparison`	Team head-to-head: qualifying, race pace, sectors
`get_driver_comparison`	Driver head-to-head: qualifying H2H, race H2H, pace delta
`get_tire_analysis`	Tire strategy, stint lengths, degradation rates

See references/api-reference.md for full parameter lists and return shapes.

Show full SKILL.md (326 more words)Show less

Examples

Example 1: F1 calendar User says: "Show me the F1 calendar" Actions:

Derive year from currentDate
Call get_race_schedule(year=<derived_year>) Result: Full calendar with event names, dates, and circuits

Example 2: Driver race performance User says: "How did Verstappen do at Monza?" Actions:

Derive year from currentDate (or from context)
Call get_race_results(year=<year>, event="Monza") for final classification
Call get_lap_data(year=<year>, event="Monza", session_type="R", driver="VER") for lap times Result: Finishing position, gap to leader, fastest lap, and tire strategy

Example 3: Latest results queried in pre-season User says: "What were the latest F1 results?" (asked in February 2026) Actions:

Current month is February → season not yet started → use year = 2025
Call get_race_schedule(year=2025) to find the last event of that season
Call get_race_results(year=2025, event=<last_event>) for the final race results Result: Results of the final 2025 race

Commands that DO NOT exist — never call these

~~get_qualifying~~ / ~~get_practice~~ — does not exist. Use get_session_data with session_type="Q" for qualifying or session_type="FP1"/"FP2"/"FP3" for practice.
~~get_standings~~ — does not exist. Use get_championship_standings instead.
~~get_results~~ — does not exist. Use get_race_results instead.
~~get_calendar~~ — does not exist. Use get_race_schedule instead.

If a command is not listed in the Commands table above, it does not exist.

Troubleshooting

Error: Event name not found Cause: Event name spelling does not match FastF1's internal naming Solution: Call get_race_schedule(year=<year>) first to get the exact event names, then retry with the correct name

Error: Session data is empty Cause: The session has not happened yet Solution: FastF1 only returns data for completed sessions. Check get_race_schedule for when the session is scheduled

Error: get_race_results returns no fastest_lap_time Cause: FastF1 has no lap data for that race (fastest_lap_time is computed from the laps) Solution: Use get_lap_data(session_type="R") and find the minimum lap_time across all drivers

Error: Querying the current year in January or February returns no data Cause: The new F1 season has not started yet Solution: Use year = current_year - 1 for any pre-March query; do not query the current year before March

Files

SKILL.md and 4 other files (scripts, references) in skills/fastf1 of machina-sports/sports-skills.

SKILL.md
references/api-reference.md
references/commands.md
references/schemas.md
scripts/validate_params.sh

Open the folder on GitHubat commit 0420a7c

Compare with similar skills

Fastf1 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Fastf1 compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Fastf1 this skillmachina-sports/sports-skills	245	—	~1.6k	Automated safety check: Pass	MIT	today
Prediction MarketsBlockRunAI/blockrun-mcp	391	—	~3.7k	Automated safety check: Pass	MIT	3 days ago
Predexon Prediction Market DataBlockRunAI/ClawRouter	6.6k	—	~4.7k	Automated safety check: Pass	MIT	6 days ago
Surf Crypto Data APIBlockRunAI/blockrun-mcp	391	—	~1.7k	Automated safety check: Pass	MIT	3 days ago
Digital Oraclekomako-workshop/digital-oracle	878	—	~5.9k	Automated safety check: Pass	MIT	2 mo ago
Dr Manhattanguzus/dr-manhattan	204	—	~2k	Automated safety check: Pass	Apache-2.0	2 mo ago

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Questions about Fastf1

What does Fastf1 do?

Formula 1 data — race schedules, results, lap timing, driver and team info. Fastf1 is an agent skill from machina-sports/sports-skills. Formula 1 data — race schedules, results, lap timing, driver and team info.

When should I use Fastf1?

Fastf1 fits situations like: : user asks about F1 race results; the F1 calendar; : user asks about other motorsports (MotoGP; F1 betting odds.

How do I install Fastf1 in Claude Code?

Run `npx skills add machina-sports/sports-skills --skill fastf1 -a claude-code`. Or copy the skill folder (skills/fastf1 in machina-sports/sports-skills) into .claude/skills/fastf1 in your project. Claude Code loads it when a task matches its description.

How do I install Fastf1 in Codex?

Run `npx skills add machina-sports/sports-skills --skill fastf1 -a codex`. Or copy the skill folder (skills/fastf1 in machina-sports/sports-skills) into .agents/skills/fastf1 in your project. Codex loads it when a task matches its description.

Can I use Fastf1 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add machina-sports/sports-skills --skill fastf1 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fastf1, .gemini/skills/fastf1, .github/skills/fastf1 and .opencode/skills/fastf1 in your project.

What does Fastf1 need to run?

Going by SKILL.md and its folder, Fastf1 needs a shell for the scripts in its folder. Our summary lists: Python 3; A Bash shell. Compatibility (from SKILL.md): Requires Python 3.10+ (install with: pip install sports-skills).

Does Fastf1 access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Fastf1 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Fastf1 use?

Fastf1 is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Fastf1 use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.

What are the alternatives to Fastf1?

Skills that share tags, products or a category with Fastf1: Prediction Markets (BlockRunAI/blockrun-mcp, 391 stars), Predexon Prediction Market Data (BlockRunAI/ClawRouter, 6.6k stars), Surf Crypto Data API (BlockRunAI/blockrun-mcp, 391 stars) and Digital Oracle (komako-workshop/digital-oracle, 878 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fastf1?

machina-sports (a GitHub organization) maintains it in machina-sports/sports-skills, which has 245 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 10, 2026.

Source: machina-sports/sports-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Fastf1

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

FastF1 — Formula 1 Data

Quick Start

CRITICAL: Before Any Query

Choosing the Year

Workflows

Race Weekend Analysis

Driver/Team Comparison

Season Overview

Commands

Examples

Commands that DO NOT exist — never call these

Troubleshooting

Files

Compare with similar skills

Similar skills

More from machina-sports/sports-skills

Related

Questions about Fastf1