GitHub - eddmann/strava-cli: Strava from your terminal. Pipe it, script it, automate it.

Strava from your terminal. Pipe it, script it, automate it.

Exploring CLI tools as skills for AI agents. Background below.

Features

All your Strava data — activities, stats, segments, routes, clubs, gear
Script and automate — composable with jq, pipes, xargs, and standard Unix tools
AI agent ready — install the skill for Claude, Cursor, and other assistants
Flexible output — JSON for scripts, CSV for spreadsheets, tables for humans

Installation

Quick Install (Recommended)

curl -fsSL https://raw.githubusercontent.com/eddmann/strava-cli/main/install.sh | sh

Downloads the pre-built binary for your platform (macOS/Linux) to ~/.local/bin.

Homebrew

brew install eddmann/tap/strava-cli

Using uv/pipx

Requires Python 3.10+ and uv.

uv tool install git+https://github.com/eddmann/strava-cli.git

From Source

git clone https://github.com/eddmann/strava-cli
cd strava-cli
uv sync
uv run strava --help

Quick Start

1. Authenticate

First time? You'll be guided to create a Strava API application:

Go to https://www.strava.com/settings/api
Create an application (set callback domain to localhost)
Enter your Client ID and Client Secret when prompted

Credentials and tokens are stored in ~/.config/strava-cli/config.toml.

2. Use the CLI

# List recent activities
strava activities list --limit 10

# Get a specific activity
strava activities get 12345678

# Get your athlete profile
strava athlete

# Get your stats
strava athlete stats

Command Reference

Global Options

Flag	Short	Description
`--format`	`-f`	Output format: `json` (default), `jsonl`, `csv`, `tsv`, `human`
`--fields`		Comma-separated list of fields to include
`--no-header`		Omit header row in CSV/TSV output
`--verbose`	`-v`	Verbose output to stderr
`--quiet`	`-q`	Suppress non-essential output
`--config`	`-c`	Path to config file
`--profile`	`-p`	Named profile to use
`--version`	`-V`	Show version and exit

Context (LLM Helper)

strava context                      # Get full context: athlete, stats, gear, clubs, recent activities
strava context --activities 10      # Include more recent activities
strava context --focus stats,gear   # Only specific sections
strava context --no-clubs           # Exclude clubs

Authentication

strava auth login          # OAuth browser flow
strava auth logout         # Clear stored credentials
strava auth logout --revoke  # Also revoke on Strava servers
strava auth status         # Show authentication status
strava auth refresh        # Force token refresh

Activities

strava activities list [--after DATE] [--before DATE] [--limit N]
strava activities get <ID> [--include-efforts]
strava activities create --name NAME --sport-type TYPE --start ISO8601 --elapsed SECS
strava activities update <ID> [--name NAME] [--description DESC]
strava activities delete <ID> [--force]
strava activities streams <ID> [--keys time,distance,heartrate,...]
strava activities laps <ID>
strava activities zones <ID>
strava activities comments <ID>
strava activities kudos <ID>

Athlete

strava athlete             # Get profile
strava athlete stats       # Get statistics
strava athlete zones       # Get HR/power zones

Segments

strava segments get <ID>
strava segments starred [--limit N]
strava segments star <ID>
strava segments unstar <ID>
strava segments explore --bounds SW_LAT,SW_LNG,NE_LAT,NE_LNG [--activity-type running|riding]

Segment Efforts

strava efforts get <ID>
strava efforts list --segment-id <ID> [--start DATE] [--end DATE]

Routes

strava routes list [--limit N]
strava routes get <ID>
strava routes export <ID> --format gpx|tcx [-o FILE]
strava routes streams <ID>

Clubs

strava clubs list
strava clubs get <ID>
strava clubs members <ID> [--limit N]
strava clubs activities <ID> [--limit N]

Gear

strava gear get <GEAR_ID>   # e.g., b12345678 for bikes, g12345678 for shoes

Upload

strava upload <FILE> [--data-type fit|gpx|tcx] [--name NAME] [--wait]
strava upload status <UPLOAD_ID>

Composability

# Filter runs over 10km (distance in meters)
strava activities list | jq '.[] | select(.sport_type=="Run" and .distance > 10000)'

# Total distance this month in km
strava activities list --after 2025-12-01 | jq '[.[].distance] | add / 1000'

# Calculate pace for recent runs (min/km)
strava activities list --limit 5 | jq '.[] | select(.sport_type=="Run") | {name, pace: ((.moving_time/60) / (.distance/1000) | floor)}'

Configuration

Config file location: ~/.config/strava-cli/config.toml

[client]
id = "12345"
secret = "abc123..."

[auth]
access_token = "..."
refresh_token = "..."
expires_at = 1234567890
athlete_id = 12345
scopes = ["read", "activity:read_all", "activity:write"]

[defaults]
format = "json"
limit = 30

# Multiple profiles for different accounts
[profiles.work]
access_token = "..."
refresh_token = "..."

Environment Variables

Variable	Description
`STRAVA_CLIENT_ID`	OAuth client ID (or use config file)
`STRAVA_CLIENT_SECRET`	OAuth client secret (or use config file)
`STRAVA_ACCESS_TOKEN`	Override access token
`STRAVA_REFRESH_TOKEN`	Override refresh token
`STRAVA_CONFIG`	Config file path
`STRAVA_FORMAT`	Default output format
`STRAVA_PROFILE`	Default profile

AI Agent Integration

This CLI is available as an Agent Skill — it works with Claude Code, Cursor, and other compatible AI agents. See SKILL.md for the skill definition.

Install Agent Skill

curl -fsSL https://raw.githubusercontent.com/eddmann/strava-cli/main/install-skill.sh | sh

Installs the skill to ~/.claude/skills/strava/ and ~/.cursor/skills/strava/. Agents will auto-detect when you ask about Strava/fitness data.

Development

git clone https://github.com/eddmann/strava-cli
cd strava-cli
make install                          # Install dependencies
make test                             # Run tests
make run CMD="activities list --limit 5"  # Run command

Background

I recently built strava-mcp, an MCP server for Strava. This got me thinking about alternative approaches to giving AI agents capabilities.

There's been a lot of discussion around the heavyweight nature of MCP. An alternative approach is to give agents discoverable skills via well-documented CLI tooling. Give an LLM a terminal and let it use composable CLI tools to build up functionality and solve problems — the Unix philosophy applied to AI agents.

This project is an exploration of Claude Code Skills and the emerging Agent Skills standard for AI-tool interoperability. The goal was to build a CLI that works seamlessly as both:

A traditional Unix tool — composable, pipe-friendly, machine-readable
An AI agent skill — structured output, comprehensive documentation, predictable behavior

Going forward, another approach worth exploring is going one step further than CLI and providing a code library that agents can import and use directly.

License

MIT

Credits

Built on top of stravalib.