According to AlphaMoat, as of 2026-09, Vision Tagger has 2,442 downloads and 0 stars — ranked #4,149 of 5,078 AI skills overall, and #406 of 512 in Design & Media.

Installs

135

Downloads

2.4K

Stars

Overall Rank

#4,149

Top 82% overall · Top 80% in Design & Media

Platform	Installs	Downloads	Stars	Comments
ClawHub ↗	74	2.4K	-	-
SkillHub ↗	135	2.4K	-	—

Platforms count differently and update at different times.

What this skill does

This skill uses Apple Vision for local image processing on macOS and does not require a cloud API or API key. It can produce structured JSON and optionally draw annotation boxes on an image. It requires macOS, Xcode Command Line Tools, Python, and Pillow. Results may contain faces, pose information, recognized text, or barcode contents. Process only images the user owns or is authorized to analyze, and treat the JSON and annotated images as sensitive data. The documented minimum is macOS 12.

Profile published 2026-10-29

What you can do with it

Extract visible content and region annotations from photos, documents, or other images locally

When to use it

You need local computer-vision analysis on macOS

Example prompts

Generated from the skill’s documentation as examples; not results of actual runs.

Analyze an authorized image and produce structured annotations while explaining how to protect sensitive output

Monthly Trend

Downloads2.4K

Stars-

The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.

Month	Downloads	MoM	Stars
2026-09	2.4K	—	-
2026-08	2.3K	+6.7%	-
2026-07	2.2K	+11.6%	-
2026-06	2.0K	+9.0%	-
2026-05	1.8K	—	-
2026-03	1.3K	—	-

SKILL.md (original)

The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.

3.7 KB · ClawHub v1.0.0

---
name: vision-tagger
description: Tag and annotate images using Apple Vision framework (macOS only). Detects faces, bodies, hands, text (OCR), barcodes, objects, scene labels, and saliency regions. Use for image analysis, photo tagging, posture monitoring, or any task requiring computer vision on images.
homepage: https://clawhub.ai/skills/vision-tagger
metadata: {"clawdbot":{"emoji":"👁️","requires":{"os":"macos","bins":["swiftc","python3"]},"install":[{"id":"xcode","kind":"shell","command":"xcode-select --install","label":"Install Xcode CLI tools"},{"id":"pillow","kind":"shell","command":"pip3 install Pillow","label":"Install Pillow"}]}}
---

# Vision Tagger

macOS-native image analysis using Apple's Vision framework. All processing is local — no cloud APIs, no API keys needed.

Show all (110 lines)


## Requirements

- macOS 12+ (Monterey or later)
- Xcode Command Line Tools
- Python 3 with Pillow

## Setup (one-time)

```bash
# Install Xcode CLI tools if needed
xcode-select --install

# Install Pillow
pip3 install Pillow

# Compile the Swift binary
cd scripts/
swiftc -O -o image_tagger image_tagger.swift
```

## Usage

### Analyze image → JSON

```bash
./scripts/image_tagger /path/to/photo.jpg
```

Output includes:
- `faces` — bounding boxes, roll/yaw/pitch, landmarks (eyes, nose, mouth)
- `bodies` — 18 skeleton joints with confidence scores
- `hands` — 21 joints per hand (left/right)
- `text` — OCR results with bounding boxes
- `labels` — scene classification (desk, outdoor, clothing, etc.)
- `barcodes` — QR codes, UPC, etc.
- `saliency` — attention and objectness regions

### Annotate image with boxes

```bash
python3 scripts/annotate_image.py photo.jpg output.jpg
```

Draws colored boxes:
- 🟢 Green: faces
- 🟠 Orange: body skeleton
- 🟣 Magenta: hands
- 🔵 Cyan: text regions
- 🟡 Yellow: rectangles/objects
- Scene labels at bottom

### Python integration

```python
import subprocess, json

def analyze(path):
    r = subprocess.run(['./scripts/image_tagger', path], capture_output=True, text=True)
    return json.loads(r.stdout[r.stdout.find('{'):])

tags = analyze('photo.jpg')
print(tags['labels'])  # [{'label': 'desk', 'confidence': 0.85}, ...]
print(tags['faces'])   # [{'bbox': {...}, 'confidence': 0.99, 'yaw': 5.2}]
```

## Example JSON Output

```json
{
  "dimensions": {"width": 1920, "height": 1080},
  "faces": [{"bbox": {"x": 0.3, "y": 0.4, "width": 0.15, "height": 0.2}, "confidence": 0.99, "roll": -2, "yaw": 5}],
  "bodies": [{"joints": {"head_joint": {"x": 0.5, "y": 0.7, "confidence": 0.9}, "left_shoulder": {...}}, "confidence": 1}],
  "hands": [{"chirality": "left", "joints": {"VNHLKWRI": {"x": 0.4, "y": 0.3, "confidence": 0.85}}}],
  "text": [{"text": "HELLO", "confidence": 0.95, "bbox": {...}}],
  "labels": [{"label": "outdoor", "confidence": 0.88}, {"label": "sky", "confidence": 0.75}],
  "saliency": {"attentionBased": [{"x": 0.2, "y": 0.1, "width": 0.6, "height": 0.8}]}
}
```

## Detection Capabilities

| Feature | Details |
|---------|---------|
| Faces | Bounding box, confidence, roll/yaw/pitch angles, 76-point landmarks |
| Bodies | 18 joints: head, neck, shoulders, elbows, wrists, hips, knees, ankles |
| Hands | 21 joints per hand, left/right chirality |
| Text (OCR) | Recognized text with confidence and bounding boxes |
| Labels | 1000+ scene/object categories (clothing, furniture, outdoor, etc.) |
| Barcodes | QR, UPC, EAN, Code128, PDF417, Aztec, DataMatrix |
| Saliency | Attention-based and objectness-based regions |

## Use Cases

- **Photo tagging** — Auto-tag photos with detected objects/scenes
- **Posture monitoring** — Track face/body position for ergonomics
- **Document scanning** — Extract text from images
- **Security** — Detect people in camera feeds
- **Accessibility** — Describe image contents