# Send Vision Tagger to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "vision-tagger",
    "name": "Vision Tagger",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/sagarjhaa/vision-tagger",
    "canonicalUrl": "https://clawhub.ai/sagarjhaa/vision-tagger",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/vision-tagger",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vision-tagger",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "SKILL.md",
      "scripts/setup.sh",
      "scripts/image_tagger.swift",
      "scripts/annotate_image.py"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "slug": "vision-tagger",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-04T10:24:53.311Z",
      "expiresAt": "2026-05-11T10:24:53.311Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vision-tagger",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vision-tagger",
        "contentDisposition": "attachment; filename=\"vision-tagger-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null,
        "slug": "vision-tagger"
      },
      "scope": "item",
      "summary": "Item download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/vision-tagger"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/vision-tagger",
    "downloadUrl": "https://openagent3.xyz/downloads/vision-tagger",
    "agentUrl": "https://openagent3.xyz/skills/vision-tagger/agent",
    "manifestUrl": "https://openagent3.xyz/skills/vision-tagger/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/vision-tagger/agent.md"
  }
}
```
## Documentation

### Vision Tagger

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

### Requirements

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

### Setup (one-time)

# 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

### Analyze image → JSON

./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

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

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

{
  "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

FeatureDetailsFacesBounding box, confidence, roll/yaw/pitch angles, 76-point landmarksBodies18 joints: head, neck, shoulders, elbows, wrists, hips, knees, anklesHands21 joints per hand, left/right chiralityText (OCR)Recognized text with confidence and bounding boxesLabels1000+ scene/object categories (clothing, furniture, outdoor, etc.)BarcodesQR, UPC, EAN, Code128, PDF417, Aztec, DataMatrixSaliencyAttention-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
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: sagarjhaa
- Version: 1.0.0
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-05-04T10:24:53.311Z
- Expires at: 2026-05-11T10:24:53.311Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/vision-tagger)
- [Send to Agent page](https://openagent3.xyz/skills/vision-tagger/agent)
- [JSON manifest](https://openagent3.xyz/skills/vision-tagger/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/vision-tagger/agent.md)
- [Download page](https://openagent3.xyz/downloads/vision-tagger)