# etapa

> Use this tool when you need to create personalized cycling training plans or provide beginner-friendly guidance on getting started with cycling. Etapa's AI coach generates tailored 2-4 week plans based on fitness, goals, and availability, and offers plain-English advice on essential topics like bike choice, safety, and nutrition. Ideal for beginners or returning riders who want to start cycling without being overwhelmed by technical jargon.

Canonical page: https://skillsregistry.net/skills/honeybulr-etapa  
JSON: https://api.skillsregistry.net/v1/skills/honeybulr-etapa

## Description

Etapa is an AI cycling coach for beginners and returning riders. Most cycling apps assume you already speak the language (FTP, TSS, zone 2). Etapa doesn't. The MCP exposes two tools: `generate_training_plan` — produces a personalised 2-4 week plan tailored to the rider's fitness, goal, and available days via the Etapa API; and `cycling_beginner_guide` — plain-English guidance on choosing a first bike, essential gear, first rides, nutrition, road safety, bike fit, and building a habit. Free, no account, no jargon.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/honeybulr/etapa)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "honeybulr-etapa"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/honeybulr-etapa` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/honeybulr-etapa/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt