--- name: azure-ai-contentunderstanding-py-v2 description: "Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: content-media tags: ["azure-ai-contentunderstanding-py-v2", "azure-ai-contentunderstanding-py", "azure", "content", "understanding", "sdk", "for", "python"] complexity: intermediate risk: caution tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-19" date_updated: "2026-04-25" --- # Azure AI Content Understanding SDK for Python ## Overview This public intake copy packages `plugins/antigravity-awesome-skills/skills/azure-ai-contentunderstanding-py` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin. 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 the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review. # Azure AI Content Understanding SDK for Python Multimodal AI service that extracts semantic content from documents, video, audio, and image files for RAG and automated workflows. 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, Prebuilt Analyzers, Analyze Document, Access Document Content Details, Analyze Image. ## 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 Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. - 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 | Situation | Start here | Why it matters | | --- | --- | --- | | First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow | | Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source | | Workflow execution | `SKILL.md` | Starts with the smallest copied file that materially changes execution | | Supporting context | `SKILL.md` | Adds the next most relevant copied source file without loading the entire package | | Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts | ## 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. Begin Analysis — Start the analysis operation with begin_analyze() (returns a poller) 2. Poll for Results — Poll until analysis completes (SDK handles this with .result()) 3. Process Results — Extract structured results from AnalyzeResult.contents 4. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task. 5. Read the overview and provenance files before loading any copied upstream support files. 6. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request. 7. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes. ### Imported Workflow Notes #### Imported: Installation ```bash pip install azure-ai-contentunderstanding ``` #### Imported: Core Workflow Content Understanding operations are asynchronous long-running operations: 1. **Begin Analysis** — Start the analysis operation with `begin_analyze()` (returns a poller) 2. **Poll for Results** — Poll until analysis completes (SDK handles this with `.result()`) 3. **Process Results** — Extract structured results from `AnalyzeResult.contents` #### Imported: Environment Variables ```bash CONTENTUNDERSTANDING_ENDPOINT=https://.cognitiveservices.azure.com/ ``` ## Examples ### Example 1: Ask for the upstream workflow directly ```text Use @azure-ai-contentunderstanding-py-v2 to handle . 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 ```text Review @azure-ai-contentunderstanding-py-v2 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 ```text Use @azure-ai-contentunderstanding-py-v2 for . 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 ```text Review @azure-ai-contentunderstanding-py-v2 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. - Use begin_analyze with AnalyzeInput — this is the correct method signature - Access results via result.contents[0] — results are returned as a list - Use prebuilt analyzers for common scenarios (document/image/audio/video search) - Create custom analyzers only for domain-specific field extraction - Use async client for high-throughput scenarios with azure.identity.aio credentials - Handle long-running operations — video/audio analysis can take minutes - Use URL sources when possible to avoid upload overhead ### Imported Operating Notes #### Imported: Best Practices 1. **Use `begin_analyze` with `AnalyzeInput`** — this is the correct method signature 2. **Access results via `result.contents[0]`** — results are returned as a list 3. **Use prebuilt analyzers** for common scenarios (document/image/audio/video search) 4. **Create custom analyzers** only for domain-specific field extraction 5. **Use async client** for high-throughput scenarios with `azure.identity.aio` credentials 6. **Handle long-running operations** — video/audio analysis can take minutes 7. **Use URL sources** when possible to avoid upload overhead ## Troubleshooting ### Problem: The operator skipped the imported context and answered too generically **Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills/skills/azure-ai-contentunderstanding-py`, fails to mention provenance, or does not use any copied source files at all. **Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing. ### Problem: The imported workflow feels incomplete during review **Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task. **Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it. ### 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 - `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith-v2` - 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. | Resource family | What it gives the reviewer | Example path | | --- | --- | --- | | `references` | copied reference notes, guides, or background material from upstream | `references/n/a` | | `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` | | `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` | | `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` | | `assets` | supporting assets or schemas copied from the source package | `assets/n/a` | ### Imported Reference Notes #### Imported: Authentication ```python import os from azure.ai.contentunderstanding import ContentUnderstandingClient from azure.identity import DefaultAzureCredential endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"] credential = DefaultAzureCredential() client = ContentUnderstandingClient(endpoint=endpoint, credential=credential) ``` #### Imported: Prebuilt Analyzers | Analyzer | Content Type | Purpose | |----------|--------------|---------| | `prebuilt-documentSearch` | Documents | Extract markdown for RAG applications | | `prebuilt-imageSearch` | Images | Extract content from images | | `prebuilt-audioSearch` | Audio | Transcribe audio with timing | | `prebuilt-videoSearch` | Video | Extract frames, transcripts, summaries | | `prebuilt-invoice` | Documents | Extract invoice fields | #### Imported: Analyze Document ```python import os from azure.ai.contentunderstanding import ContentUnderstandingClient from azure.ai.contentunderstanding.models import AnalyzeInput from azure.identity import DefaultAzureCredential endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"] client = ContentUnderstandingClient( endpoint=endpoint, credential=DefaultAzureCredential() ) # Analyze document from URL poller = client.begin_analyze( analyzer_id="prebuilt-documentSearch", inputs=[AnalyzeInput(url="https://example.com/document.pdf")] ) result = poller.result() # Access markdown content (contents is a list) content = result.contents[0] print(content.markdown) ``` #### Imported: Access Document Content Details ```python from azure.ai.contentunderstanding.models import MediaContentKind, DocumentContent content = result.contents[0] if content.kind == MediaContentKind.DOCUMENT: document_content: DocumentContent = content # type: ignore print(document_content.start_page_number) ``` #### Imported: Analyze Image ```python from azure.ai.contentunderstanding.models import AnalyzeInput poller = client.begin_analyze( analyzer_id="prebuilt-imageSearch", inputs=[AnalyzeInput(url="https://example.com/image.jpg")] ) result = poller.result() content = result.contents[0] print(content.markdown) ``` #### Imported: Analyze Video ```python from azure.ai.contentunderstanding.models import AnalyzeInput poller = client.begin_analyze( analyzer_id="prebuilt-videoSearch", inputs=[AnalyzeInput(url="https://example.com/video.mp4")] ) result = poller.result() # Access video content (AudioVisualContent) content = result.contents[0] # Get transcript phrases with timing for phrase in content.transcript_phrases: print(f"[{phrase.start_time} - {phrase.end_time}]: {phrase.text}") # Get key frames (for video) for frame in content.key_frames: print(f"Frame at {frame.time}: {frame.description}") ``` #### Imported: Analyze Audio ```python from azure.ai.contentunderstanding.models import AnalyzeInput poller = client.begin_analyze( analyzer_id="prebuilt-audioSearch", inputs=[AnalyzeInput(url="https://example.com/audio.mp3")] ) result = poller.result() # Access audio transcript content = result.contents[0] for phrase in content.transcript_phrases: print(f"[{phrase.start_time}] {phrase.text}") ``` #### Imported: Custom Analyzers Create custom analyzers with field schemas for specialized extraction: ```python # Create custom analyzer analyzer = client.create_analyzer( analyzer_id="my-invoice-analyzer", analyzer={ "description": "Custom invoice analyzer", "base_analyzer_id": "prebuilt-documentSearch", "field_schema": { "fields": { "vendor_name": {"type": "string"}, "invoice_total": {"type": "number"}, "line_items": { "type": "array", "items": { "type": "object", "properties": { "description": {"type": "string"}, "amount": {"type": "number"} } } } } } } ) # Use custom analyzer from azure.ai.contentunderstanding.models import AnalyzeInput poller = client.begin_analyze( analyzer_id="my-invoice-analyzer", inputs=[AnalyzeInput(url="https://example.com/invoice.pdf")] ) result = poller.result() # Access extracted fields print(result.fields["vendor_name"]) print(result.fields["invoice_total"]) ``` #### Imported: Analyzer Management ```python # List all analyzers analyzers = client.list_analyzers() for analyzer in analyzers: print(f"{analyzer.analyzer_id}: {analyzer.description}") # Get specific analyzer analyzer = client.get_analyzer("prebuilt-documentSearch") # Delete custom analyzer client.delete_analyzer("my-custom-analyzer") ``` #### Imported: Async Client ```python import asyncio import os from azure.ai.contentunderstanding.aio import ContentUnderstandingClient from azure.ai.contentunderstanding.models import AnalyzeInput from azure.identity.aio import DefaultAzureCredential async def analyze_document(): endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"] credential = DefaultAzureCredential() async with ContentUnderstandingClient( endpoint=endpoint, credential=credential ) as client: poller = await client.begin_analyze( analyzer_id="prebuilt-documentSearch", inputs=[AnalyzeInput(url="https://example.com/doc.pdf")] ) result = await poller.result() content = result.contents[0] return content.markdown asyncio.run(analyze_document()) ``` #### Imported: Content Types | Class | For | Provides | |-------|-----|----------| | `DocumentContent` | PDF, images, Office docs | Pages, tables, figures, paragraphs | | `AudioVisualContent` | Audio, video files | Transcript phrases, timing, key frames | Both derive from `MediaContent` which provides basic info and markdown representation. #### Imported: Model Imports ```python from azure.ai.contentunderstanding.models import ( AnalyzeInput, AnalyzeResult, MediaContentKind, DocumentContent, AudioVisualContent, ) ``` #### Imported: Client Types | Client | Purpose | |--------|---------| | `ContentUnderstandingClient` | Sync client for all operations | | `ContentUnderstandingClient` (aio) | Async client for all operations | #### 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.