# efferent-ble-simulator

> efferent-ble-simulator — dncore-efferent-ble-simulator. Use this tool when you need to simulate Bluetooth LE fitness devices for testing and training AI agents, enabling them to interact with virtual devices such as smart trainers and heart rate monitors via stdio or HTTP interfaces. It solves problems related to device compatibility, data parsing, and real-time monitoring, providing a controlled environment for AI development. Ideal for use cases requiring realistic Bluetooth LE device simulations, such as fitness tracking, sports analytics, and wearable technology integration.

Canonical page: https://skillsregistry.net/skills/dncore-efferent-ble-simulator  
JSON: https://api.skillsregistry.net/v1/skills/dncore-efferent-ble-simulator

## Description

Enables AI agents to start, configure, and monitor simulated Bluetooth LE fitness devices such as smart trainers, power meters, speed/cadence sensors, and heart rate monitors over real Linux BlueZ radios via stdio or HTTP.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/l0hc5hklhe)
- **Repository:** <https://github.com/dncore/efferent-ble-simulator>

## 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": "dncore-efferent-ble-simulator"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/dncore-efferent-ble-simulator` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/dncore-efferent-ble-simulator/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