Azure AI Vision Image Analysis SDK for Python

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/azure-ai-vision-imageanalysis-py

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Azure AI Vision Image Analysis SDK for Python Client library for Azure AI Vision 4.0 image analysis including captions, tags, objects, OCR, and more.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Analyze Image from URL, Analyze Image from File, Image Caption, Dense Captions (Multiple Regions).

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. bash pip install azure-ai-vision-imageanalysis
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: Installation

pip install azure-ai-vision-imageanalysis

Imported: Environment Variables

VISION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
VISION_KEY=<your-api-key>  # If using API key

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-ai-vision-imageanalysis-py to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @azure-ai-vision-imageanalysis-py against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @azure-ai-vision-imageanalysis-py for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @azure-ai-vision-imageanalysis-py using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Select only needed features to optimize latency and cost
• Use async client for high-throughput scenarios
• Handle HttpResponseError for invalid images or auth issues
• Enable genderneutralcaption for inclusive descriptions
• Specify language for localized captions
• Use smartcropsaspect_ratios matching your thumbnail requirements
• Cache results when analyzing the same image multiple times

Imported Operating Notes

Imported: Best Practices

1. Select only needed features to optimize latency and cost
2. Use async client for high-throughput scenarios
3. Handle HttpResponseError for invalid images or auth issues
4. Enable gender_neutral_caption for inclusive descriptions
5. Specify language for localized captions
6. Use smart_crops_aspect_ratios matching your thumbnail requirements
7. Cache results when analyzing the same image multiple times

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/azure-ai-vision-imageanalysis-py

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @ai-dev-jobs-mcp

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @arm-cortex-expert

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @asana-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ask-questions-if-underspecified

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Authentication

API Key

import os
from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.core.credentials import AzureKeyCredential

endpoint = os.environ["VISION_ENDPOINT"]
key = os.environ["VISION_KEY"]

client = ImageAnalysisClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key)
)

Entra ID (Recommended)

from azure.ai.vision.imageanalysis import ImageAnalysisClient
from azure.identity import DefaultAzureCredential

client = ImageAnalysisClient(
    endpoint=os.environ["VISION_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Imported: Analyze Image from URL

from azure.ai.vision.imageanalysis.models import VisualFeatures

image_url = "https://example.com/image.jpg"

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[
        VisualFeatures.CAPTION,
        VisualFeatures.TAGS,
        VisualFeatures.OBJECTS,
        VisualFeatures.READ,
        VisualFeatures.PEOPLE,
        VisualFeatures.SMART_CROPS,
        VisualFeatures.DENSE_CAPTIONS
    ],
    gender_neutral_caption=True,
    language="en"
)

Imported: Analyze Image from File

with open("image.jpg", "rb") as f:
    image_data = f.read()

result = client.analyze(
    image_data=image_data,
    visual_features=[VisualFeatures.CAPTION, VisualFeatures.TAGS]
)

Imported: Image Caption

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.CAPTION],
    gender_neutral_caption=True
)

if result.caption:
    print(f"Caption: {result.caption.text}")
    print(f"Confidence: {result.caption.confidence:.2f}")

Imported: Dense Captions (Multiple Regions)

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.DENSE_CAPTIONS]
)

if result.dense_captions:
    for caption in result.dense_captions.list:
        print(f"Caption: {caption.text}")
        print(f"  Confidence: {caption.confidence:.2f}")
        print(f"  Bounding box: {caption.bounding_box}")

Imported: Tags

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.TAGS]
)

if result.tags:
    for tag in result.tags.list:
        print(f"Tag: {tag.name} (confidence: {tag.confidence:.2f})")

Imported: Object Detection

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.OBJECTS]
)

if result.objects:
    for obj in result.objects.list:
        print(f"Object: {obj.tags[0].name}")
        print(f"  Confidence: {obj.tags[0].confidence:.2f}")
        box = obj.bounding_box
        print(f"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

Imported: OCR (Text Extraction)

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.READ]
)

if result.read:
    for block in result.read.blocks:
        for line in block.lines:
            print(f"Line: {line.text}")
            print(f"  Bounding polygon: {line.bounding_polygon}")
            
            # Word-level details
            for word in line.words:
                print(f"  Word: {word.text} (confidence: {word.confidence:.2f})")

Imported: People Detection

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.PEOPLE]
)

if result.people:
    for person in result.people.list:
        print(f"Person detected:")
        print(f"  Confidence: {person.confidence:.2f}")
        box = person.bounding_box
        print(f"  Bounding box: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

Imported: Smart Cropping

result = client.analyze_from_url(
    image_url=image_url,
    visual_features=[VisualFeatures.SMART_CROPS],
    smart_crops_aspect_ratios=[0.9, 1.33, 1.78]  # Portrait, 4:3, 16:9
)

if result.smart_crops:
    for crop in result.smart_crops.list:
        print(f"Aspect ratio: {crop.aspect_ratio}")
        box = crop.bounding_box
        print(f"  Crop region: x={box.x}, y={box.y}, w={box.width}, h={box.height}")

Imported: Async Client

from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
from azure.identity.aio import DefaultAzureCredential

async def analyze_image():
    async with ImageAnalysisClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        result = await client.analyze_from_url(
            image_url=image_url,
            visual_features=[VisualFeatures.CAPTION]
        )
        print(result.caption.text)

Imported: Visual Features

CAPTION

DENSE_CAPTIONS

TAGS

OBJECTS

READ

PEOPLE

SMART_CROPS

Imported: Error Handling

from azure.core.exceptions import HttpResponseError

try:
    result = client.analyze_from_url(
        image_url=image_url,
        visual_features=[VisualFeatures.CAPTION]
    )
except HttpResponseError as e:
    print(f"Status code: {e.status_code}")
    print(f"Reason: {e.reason}")
    print(f"Message: {e.error.message}")

Imported: Image Requirements

• Formats: JPEG, PNG, GIF, BMP, WEBP, ICO, TIFF, MPO
• Max size: 20 MB
• Dimensions: 50x50 to 16000x16000 pixels

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.