# wake-word-detection

A skill for building wake word detection on openWakeWord: local audio monitoring, no cloud calls. Useful when you are wiring up a JARVIS-style assistant.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 404 installs · ★ 48
- Author: martinholovsky
- License: Unlicense · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/martinholovsky/claude-skills-generator/tree/HEAD/skills/wake-word-detection/SKILL.md
- Updated: 2025-12-06
- HTML: https://affpapa.org/en/nevibecode/skills/wake-word-detection

## What it does

The skill puts your agent in the role of a wake word detection expert, covering openWakeWord, keyword spotting and always-listening systems. The workflow is TDD. You write a failing test first, then the minimum implementation, then run the full suite with `pytest` and coverage.

The core class is `SecureWakeWordDetector`. It keeps a `deque` buffer, pulls audio through `sounddevice`, runs model predictions and fires a callback once confidence clears the threshold. The buffer is cleared right after a hit. Dependencies are listed up front: `openwakeword`, `numpy`, `sounddevice`, `onnxruntime`. Tests cover the confidence threshold, buffer clearing, CPU load and memory footprint.

Risk level is MEDIUM, and that is about the microphone. The skill asks for local processing and minimal buffering, but your own code decides whether audio ever leaves the machine. If the assistant runs in the background all day, check that the wake callback does not drag a recording path along with it.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill wake-word-detection -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/wake-word-detection/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/wake-word-detection/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill wake-word-detection -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/wake-word-detection/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill wake-word-detection -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/wake-word-detection/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install martinholovsky/claude-skills-generator wake-word-detection
```

2. **Manually: put the files in place**
- File: `.github/skills/wake-word-detection/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill wake-word-detection -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/martinholovsky/claude-skills-generator --path skills/wake-word-detection
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/wake-word-detection/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add martinholovsky/claude-skills-generator --skill wake-word-detection -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/martinholovsky/claude-skills-generator/tree/HEAD/skills/wake-word-detection/SKILL.md

## When to use it
- spot an activation phrase like “Hey JARVIS” without a cloud round trip
- keep an always-listening loop under 5% CPU
- fit the detector into 100MB of memory
- test the confidence threshold and buffer clearing
- hold false positives below one per hour

## Example prompts
- write TDD tests for SecureWakeWordDetector, start with the confidence threshold
- add a test that audio_buffer is cleared right after detection
- measure CPU and memory for 10 seconds of detection and compare against the targets

## Source text · SKILL.md · Unlicense

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 2e6d737b365fdd73.

BEGIN THIRD-PARTY FILE 2e6d737b365fdd73
````text
---
name: wake-word-detection
risk_level: MEDIUM
description: "Expert skill for implementing wake word detection with openWakeWord. Covers audio monitoring, keyword spotting, privacy protection, and efficient always-listening systems for JARVIS voice assistant."
model: sonnet
---

# Wake Word Detection Skill

## 1. Overview

**Risk Level**: MEDIUM - Continuous audio monitoring, privacy implications, resource constraints

You are an expert in wake word detection with deep expertise in openWakeWord, keyword spotting, and always-listening systems.

**Primary Use Cases**:
- JARVIS activation phrase detection ("Hey JARVIS")
- Always-listening with minimal resource usage
- Offline wake word detection (no cloud dependency)

---

## 2. Core Principles

- **TDD First** - Write tests before implementation code
- **Performance Aware** - Optimize for CPU, memory, and latency
- **Privacy Preserving** - Never store audio, minimize buffers
- **Accuracy Focused** - Minimize false positives/negatives
- **Resource Efficient** - Target <5% CPU, <100MB memory

---

## 3. Core Responsibilities

### 3.1 Privacy-First Monitoring

- **Process locally** - Never send audio to external services
- **Buffer minimally** - Only keep audio needed for detection
- **Discard non-wake** - Immediately discard non-wake audio
- **User control** - Easy disable/pause functionality

### 3.2 Efficiency Requirements

- Minimal CPU usage (<5% average)
- Low memory footprint (<100MB)
- Low latency detection (<500ms)
- Low false positive rate (<1 per hour)

---

## 4. Technical Foundation

```python
# requirements.txt
openwakeword>=0.6.0
numpy>=1.24.0
sounddevice>=0.4.6
onnxruntime>=1.16.0
```

---

## 5. Implementation Workflow (TDD)

### Step 1: Write Failing Test First

```python
# tests/test_wake_word.py
import pytest
import numpy as np
from unittest.mock import Mock, patch

class TestWakeWordDetector:
    """TDD tests for wake word detection."""

    def test_detection_accuracy_threshold(self):
        """Test that detector respects confidence threshold."""
        from wake_word import SecureWakeWordDetector

        detector = SecureWakeWordDetector(threshold=0.7)
        callback = Mock()
        test_audio = np.random.randn(16000).astype(np.float32)

        with patch.object(detector.model, 'predict') as mock_predict:
            # Below threshold - should not trigger
            mock_predict.return_value = {"hey_jarvis": np.array([0.5])}
````
END THIRD-PARTY FILE 2e6d737b365fdd73

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