@@ -9,6 +9,16 @@
             "source_path_from_root": "/articles/search/cognitive-search-quickstart-blob.md",
             "redirect_url": "/azure/search/search-get-started-skillset",
             "redirect_document_id": true
+        },
+                {
+            "source_path_from_root": "/articles/search/cognitive-search-tutorial-blob.md",
+            "redirect_url": "/azure/search/tutorial-skillset",
+            "redirect_document_id": true
+        },
+        {
+            "source_path_from_root": "/articles/search/cognitive-search-tutorial-blob-dotnet.md",
+            "redirect_url": "/azure/search/tutorial-skillset",
+            "redirect_document_id": false
         },
         {
             "source_path_from_root": "/articles/search/search-howto-connecting-azure-sql-database-to-azure-search-using-indexers.md",

@@ -117,7 +117,7 @@ This section supplements the [skill reference](cognitive-search-defining-skillse
 Once the basic framework of your skillset is created and Azure AI services is configured, you can focus on each individual image skill, defining inputs and source context, and mapping outputs to fields in either an index or knowledge store.
 
 > [!NOTE]
-> For an example skillset that combines image processing with downstream natural language processing, see [REST Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). It shows how to feed skill imaging output into entity recognition and key phrase extraction.
+> For an example skillset that combines image processing with downstream natural language processing, see [REST Tutorial: Use REST and AI to generate searchable content from Azure blobs](tutorial-skillset.md). It shows how to feed skill imaging output into entity recognition and key phrase extraction.
 
 ### Example inputs for image processing
 

@@ -166,7 +166,7 @@ This section supplements the [skill reference](cognitive-search-predefined-skill
 Once the basic framework of your skillset is created and Azure AI services is configured, you can focus on each individual image skill, defining inputs and source context, and mapping outputs to fields in either an index or knowledge store.
 
 > [!NOTE]
-> For an example skillset that combines image processing with downstream natural language processing, see [REST Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). It shows how to feed skill imaging output into entity recognition and key phrase extraction.
+> For an example skillset that combines image processing with downstream natural language processing, see [REST Tutorial: Use REST and AI to generate searchable content from Azure blobs](tutorial-skillset.md). It shows how to feed skill imaging output into entity recognition and key phrase extraction.
 
 ### Inputs for image processing
 

@@ -135,7 +135,7 @@ To repeat any of the above steps, [reset the indexer](search-howto-reindex.md) b
 ## Next steps
 
 + [Quickstart: Create a skillset for AI enrichment](search-get-started-skillset.md)
-+ [Tutorial: Learn about the AI enrichment REST APIs](cognitive-search-tutorial-blob.md)
++ [Tutorial: Learn about the AI enrichment REST APIs](tutorial-skillset.md)
 + [Skillset concepts](cognitive-search-working-with-skillsets.md)
 + [Knowledge store concepts](knowledge-store-concept-intro.md)
 + [Create a skillset](cognitive-search-defining-skillset.md)

@@ -71,7 +71,7 @@ For [parallel indexing](search-howto-large-index.md), distribute your data into
 ## See also
 
 + [Quickstart: Create an AI enrichment pipeline in the Azure portal](search-get-started-skillset.md)
-+ [Tutorial: Learn AI enrichment REST APIs](cognitive-search-tutorial-blob.md)
++ [Tutorial: Learn AI enrichment REST APIs](tutorial-skillset.md)
 + [How to configure blob indexers](search-howto-indexing-azure-blob-storage.md)
 + [How to define a skillset](cognitive-search-defining-skillset.md)
 + [How to map enriched fields to an index](cognitive-search-output-field-mapping.md)

@@ -81,4 +81,4 @@ For guidance on creating a custom skill, see [Define a custom interface](cogniti
 
 + [How to define a skillset](cognitive-search-defining-skillset.md)
 + [Custom Skills interface definition](cognitive-search-custom-skill-interface.md)
-+ [Tutorial: Enriched indexing with AI](cognitive-search-tutorial-blob.md)
++ [Tutorial: Enriched indexing with AI](tutorial-skillset.md)

@@ -1,24 +1,13 @@
 ---
-title: 'Tutorial: Skillsets Using C#'
-titleSuffix: Azure AI Search
-description: Use C# and the Azure SDK for .NET to create skillsets. This skillset applies AI transformations and analyses to create searchable content from images and unstructured text.
-
+manager: nitinme
 author: HeidiSteen
 ms.author: heidist
-manager: nitinme
-
 ms.service: azure-ai-search
-ms.topic: tutorial
+ms.topic: include
 ms.date: 07/11/2025
-ms.custom:
-  - devx-track-csharp
-  - devx-track-dotnet
-  - ignite-2023
 ---
 
-# C# Tutorial: Use skillsets to generate searchable content in Azure AI Search
-
-Learn how to use the [Azure SDK for .NET](https://www.nuget.org/packages/Azure.Search.Documents/) to create an [AI enrichment pipeline](cognitive-search-concept-intro.md) for content extraction and transformations during indexing.
+Learn how to use the [Azure SDK for .NET](https://www.nuget.org/packages/Azure.Search.Documents/) to create an [AI enrichment pipeline](../../cognitive-search-concept-intro.md) for content extraction and transformations during indexing.
 
 Skillsets add AI processing to raw content, making it more uniform and searchable. Once you know how skillsets work, you can support a broad range of transformations, from image analysis to natural language processing to customized processing that you provide externally.
 
@@ -34,17 +23,17 @@ In this tutorial, you:
 
 This tutorial uses C# and the [**Azure.Search.Documents**](/dotnet/api/overview/azure/search.documents-readme) client library to create a data source, index, indexer, and skillset.
 
-The [indexer](search-indexer-overview.md) drives each step in the pipeline, starting with content extraction of sample data (unstructured text and images) in a blob container on Azure Storage.
+The [indexer](../../search-indexer-overview.md) drives each step in the pipeline, starting with content extraction of sample data (unstructured text and images) in a blob container on Azure Storage.
 
-Once content is extracted, the [skillset](cognitive-search-working-with-skillsets.md) executes built-in skills from Microsoft to find and extract information. These skills include Optical Character Recognition (OCR) on images, language detection on text, key phrase extraction, and entity recognition (organizations). New information created by the skillset is sent to fields in an [index](search-what-is-an-index.md). Once the index is populated, you can use the fields in queries, facets, and filters.
+Once content is extracted, the [skillset](../../cognitive-search-working-with-skillsets.md) executes built-in skills from Microsoft to find and extract information. These skills include Optical Character Recognition (OCR) on images, language detection on text, key phrase extraction, and entity recognition (organizations). New information created by the skillset is sent to fields in an [index](../../search-what-is-an-index.md). Once the index is populated, you can use the fields in queries, facets, and filters.
 
 ## Prerequisites
 
 + An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
 
 + [Azure Storage](/azure/storage/common/storage-account-create).
 
-+ [Azure AI Search](search-create-app-portal.md).
++ [Azure AI Search](../../search-create-service-portal.md).
 
 + [Azure.Search.Documents package](https://www.nuget.org/packages/Azure.Search.Documents).
 
@@ -77,7 +66,7 @@ Once content is extracted, the [skillset](cognitive-search-working-with-skillset
 
 ### Azure AI services
 
-Built-in AI enrichment is backed by Azure AI services, including Language service and Azure AI Vision for natural language and image processing. For small workloads like this tutorial, you can use the free allocation of 20 transactions per indexer. For larger workloads, [attach an Azure AI Services multi-region resource to a skillset](cognitive-search-attach-cognitive-services.md) for Standard pricing.
+Built-in AI enrichment is backed by Azure AI services, including Language service and Azure AI Vision for natural language and image processing. For small workloads like this tutorial, you can use the free allocation of 20 transactions per indexer. For larger workloads, [attach an Azure AI Services multi-region resource to a skillset](../../cognitive-search-attach-cognitive-services.md) for Standard pricing.
 
 ### Copy a search service URL and API key
 
@@ -237,29 +226,29 @@ SearchIndexerDataSourceConnection dataSource = CreateOrUpdateDataSource(indexerC
 
 Build and run the solution. Since this is your first request, check the Azure portal to confirm the data source was created in Azure AI Search. On the search service overview page, verify the Data Sources list has a new item. You might need to wait a few minutes for the Azure portal page to refresh.
 
-  ![Data sources tile in the Azure portal](./media/cognitive-search-tutorial-blob/data-source-tile.png "Data sources tile in the Azure portal")
+  ![Data sources tile in the Azure portal](../../media/cognitive-search-tutorial-blob/data-source-tile.png "Data sources tile in the Azure portal")
 
 ### Step 2: Create a skillset
 
-In this section, you define a set of enrichment steps that you want to apply to your data. Each enrichment step is called a *skill* and the set of enrichment steps, a *skillset*. This tutorial uses [built-in skills](cognitive-search-predefined-skills.md) for the skillset:
+In this section, you define a set of enrichment steps that you want to apply to your data. Each enrichment step is called a *skill* and the set of enrichment steps, a *skillset*. This tutorial uses [built-in skills](../../cognitive-search-predefined-skills.md) for the skillset:
 
-* [Optical Character Recognition](cognitive-search-skill-ocr.md) to recognize printed and handwritten text in image files.
+* [Optical Character Recognition](../../cognitive-search-skill-ocr.md) to recognize printed and handwritten text in image files.
 
-* [Text Merger](cognitive-search-skill-textmerger.md) to consolidate text from a collection of fields into a single "merged content" field.
+* [Text Merger](../../cognitive-search-skill-textmerger.md) to consolidate text from a collection of fields into a single "merged content" field.
 
-* [Language Detection](cognitive-search-skill-language-detection.md) to identify the content's language.
+* [Language Detection](../../cognitive-search-skill-language-detection.md) to identify the content's language.
 
-* [Entity Recognition](cognitive-search-skill-entity-recognition-v3.md) for extracting the names of organizations from content in the blob container.
+* [Entity Recognition](../../cognitive-search-skill-entity-recognition-v3.md) for extracting the names of organizations from content in the blob container.
 
-* [Text Split](cognitive-search-skill-textsplit.md) to break large content into smaller chunks before calling the key phrase extraction skill and the entity recognition skill. Key phrase extraction and entity recognition accept inputs of 50,000 characters or less. A few of the sample files need splitting up to fit within this limit.
+* [Text Split](../../cognitive-search-skill-textsplit.md) to break large content into smaller chunks before calling the key phrase extraction skill and the entity recognition skill. Key phrase extraction and entity recognition accept inputs of 50,000 characters or less. A few of the sample files need splitting up to fit within this limit.
 
-* [Key Phrase Extraction](cognitive-search-skill-keyphrases.md) to pull out the top key phrases.
+* [Key Phrase Extraction](../../cognitive-search-skill-keyphrases.md) to pull out the top key phrases.
 
 During initial processing, Azure AI Search cracks each document to extract content from different file formats. Text originating in the source file is placed into a generated `content` field, one for each document. As such, set the input as `"/document/content"` to use this text. Image content is placed into a generated `normalized_images` field, specified in a skillset as `/document/normalized_images/*`.
 
 Outputs can be mapped to an index, used as input to a downstream skill, or both as is the case with language code. In the index, a language code is useful for filtering. As an input, language code is used by text analysis skills to inform the linguistic rules around word breaking.
 
-For more information about skillset fundamentals, see [How to define a skillset](cognitive-search-defining-skillset.md).
+For more information about skillset fundamentals, see [How to define a skillset](../../cognitive-search-defining-skillset.md).
 
 ### OCR skill
 
@@ -634,13 +623,13 @@ To learn more about index concepts, see [Create Index (REST API)](/rest/api/sear
 
 ### Step 4: Create and run an indexer
 
-So far you have created a data source, a skillset, and an index. These three components become part of an [indexer](search-indexer-overview.md) that pulls each piece together into a single multi-phased operation. To tie these together in an indexer, you must define field mappings.
+So far you have created a data source, a skillset, and an index. These three components become part of an [indexer](../../search-indexer-overview.md) that pulls each piece together into a single multi-phased operation. To tie these together in an indexer, you must define field mappings.
 
 * The fieldMappings are processed before the skillset, mapping source fields from the data source to target fields in an index. If field names and types are the same at both ends, no mapping is required.
 
 * The outputFieldMappings are processed after the skillset, referencing sourceFieldNames that don't exist until document cracking or enrichment creates them. The targetFieldName is a field in an index.
 
-In addition to hooking up inputs to outputs, you can also use field mappings to flatten data structures. For more information, see [How to map enriched fields to a searchable index](cognitive-search-output-field-mapping.md).
+In addition to hooking up inputs to outputs, you can also use field mappings to flatten data structures. For more information, see [How to map enriched fields to a searchable index](../../cognitive-search-output-field-mapping.md).
 
 ```csharp
 private static SearchIndexer CreateDemoIndexer(SearchIndexerClient indexerClient, SearchIndexerDataSourceConnection dataSource, SearchIndexerSkillset skillSet, SearchIndex index)
@@ -784,17 +773,25 @@ CheckIndexerOverallStatus(indexerClient, demoIndexer);
 
 In Azure AI Search tutorial console apps, we typically add a 2-second delay before running queries that return results, but because enrichment takes several minutes to complete, we'll close the console app and use another approach instead.
 
-The easiest option is [Search explorer](search-explorer.md) in the Azure portal. You can first run an empty query that returns all documents, or a more targeted search that returns new field content created by the pipeline. 
+The easiest option is [Search Explorer](../../search-explorer.md) in the Azure portal. You can first run an empty query that returns all documents, or a more targeted search that returns new field content created by the pipeline. 
 
-1. In Azure portal, in the search Overview page, select **Indexes**.
+1. In the Azure portal, in the search service pages, expand **Search Management** > **Indexes**.
 
 1. Find **`demoindex`** in the list. It should have 14 documents. If the document count is zero, the indexer is either still running or the page hasn't been refreshed yet. 
 
-1. Select **`demoindex`**. Search explorer is the first tab.
+1. Select **`demoindex`**. Search Explorer is the first tab.
 
 1. Content is searchable as soon as the first document is loaded. To verify content exists, run an unspecified query by clicking **Search**. This query returns all currently indexed documents, giving you an idea of what the index contains.
 
-1. Next, paste in the following string for more manageable results: `search=*&$select=id, languageCode, organizations`
+1. For more manageable results, switch to JSON view and set parameters to select the fields:
+
+   ```json
+   {
+       "search": "*",
+       "count": true,
+       "select": "id, languageCode, organizations"
+   }
+   ```
 
 <a name="reset"></a>
 
@@ -808,7 +805,7 @@ The sample code for this tutorial checks for existing objects and deletes them s
 
 This tutorial demonstrated the basic steps for building an enriched indexing pipeline through the creation of component parts: a data source, skillset, index, and indexer.
 
-[Built-in skills](cognitive-search-predefined-skills.md) were introduced, along with skillset definition and the mechanics of chaining skills together through inputs and outputs. You also learned that `outputFieldMappings` in the indexer definition is required for routing enriched values from the pipeline into a searchable index on an Azure AI Search service.
+[Built-in skills](../../cognitive-search-predefined-skills.md) were introduced, along with skillset definition and the mechanics of chaining skills together through inputs and outputs. You also learned that `outputFieldMappings` in the indexer definition is required for routing enriched values from the pipeline into a searchable index on an Azure AI Search service.
 
 Finally, you learned how to test results and reset the system for further iterations. You learned that issuing queries against the index returns the output created by the enriched indexing pipeline. You also learned how to check indexer status, and which objects to delete before rerunning a pipeline.
 
@@ -823,4 +820,4 @@ You can find and manage resources in the Azure portal, using the All resources o
 Now that you're familiar with all of the objects in an AI enrichment pipeline, let's take a closer look at skillset definitions and individual skills.
 
 > [!div class="nextstepaction"]
-> [How to create a skillset](cognitive-search-defining-skillset.md)
+> [How to create a skillset](../../cognitive-search-defining-skillset.md)

@@ -1,20 +1,13 @@
 ---
-title: 'Tutorial: Skillsets Using REST'
-titleSuffix: Azure AI Search
-description: Use the Search REST APIs to create skillsets. This skillset applies AI transformations and analyses to create searchable content from images and unstructured text.
-
+manager: nitinme
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
-ms.custom:
-  - ignite-2023
-ms.topic: tutorial
-ms.date: 03/31/2025
+ms.topic: include
+ms.date: 07/11/2025
 ---
 
-# REST Tutorial: Use skillsets to generate searchable content in Azure AI Search
-
-Learn how to call REST APIs that create an [AI enrichment pipeline](cognitive-search-concept-intro.md) for content extraction and transformations during indexing.
+Learn how to call REST APIs that create an [AI enrichment pipeline](../../cognitive-search-concept-intro.md) for content extraction and transformations during indexing.
 
 Skillsets add AI processing to raw content, making it more uniform and searchable. Once you know how skillsets work, you can support a broad range of transformations, from image analysis to natural language processing to customized processing that you provide externally.
 
@@ -30,17 +23,17 @@ In this tutorial, you:
 
 This tutorial uses a REST client and the [Azure AI Search REST APIs](/rest/api/searchservice/) to create a data source, index, indexer, and skillset.
 
-The [indexer](search-indexer-overview.md) drives each step in the pipeline, starting with content extraction of sample data (unstructured text and images) in a blob container on Azure Storage.
+The [indexer](../../search-indexer-overview.md) drives each step in the pipeline, starting with content extraction of sample data (unstructured text and images) in a blob container on Azure Storage.
 
-Once content is extracted, the [skillset](cognitive-search-working-with-skillsets.md) executes built-in skills from Microsoft to find and extract information. These skills include Optical Character Recognition (OCR) on images, language detection on text, key phrase extraction, and entity recognition (organizations). New information created by the skillset is sent to fields in an [index](search-what-is-an-index.md). Once the index is populated, you can use the fields in queries, facets, and filters.
+Once content is extracted, the [skillset](../../cognitive-search-working-with-skillsets.md) executes built-in skills from Microsoft to find and extract information. These skills include Optical Character Recognition (OCR) on images, language detection on text, key phrase extraction, and entity recognition (organizations). New information created by the skillset is sent to fields in an [index](../../search-what-is-an-index.md). Once the index is populated, you can use the fields in queries, facets, and filters.
 
 ## Prerequisites
 
 + An Azure account with an active subscription. [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F).
 
 + [Azure Storage](/azure/storage/common/storage-account-create)
 
-+ [Azure AI Search](search-create-app-portal.md)
++ [Azure AI Search](../../search-create-service-portal.md)
 
 + [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client)
 
@@ -73,7 +66,7 @@ Download a zip file of the sample data repository and extract the contents. [Lea
 
 ### Azure AI services
 
-Built-in AI enrichment is backed by Azure AI services, including Language service and Azure AI Vision for natural language and image processing. For small workloads like this tutorial, you can use the free allocation of twenty transactions per indexer. For larger workloads, [attach an Azure AI Services multi-region resource to a skillset](cognitive-search-attach-cognitive-services.md) for Standard pricing.
+Built-in AI enrichment is backed by Azure AI services, including Language service and Azure AI Vision for natural language and image processing. For small workloads like this tutorial, you can use the free allocation of twenty transactions per indexer. For larger workloads, [attach an Azure AI Services multi-region resource to a skillset](../../cognitive-search-attach-cognitive-services.md) for Standard pricing.
 
 ### Copy a search service URL and API key
 
@@ -83,11 +76,11 @@ For this tutorial, connections to Azure AI Search require an endpoint and an API
 
 1. Under **Settings** > **Keys**, copy an admin key. Admin keys are used to add, modify, and delete objects. There are two interchangeable admin keys. Copy either one.
 
-   :::image type="content" source="media/search-get-started-rest/get-url-key.png" alt-text="Screenshot of the URL and API keys in the Azure portal.":::
+   :::image type="content" source="../../media/search-get-started-rest/get-url-key.png" alt-text="Screenshot of the URL and API keys in the Azure portal.":::
 
 ## Set up your REST file
 
-1. Start Visual Studio Code and open the [skillset-tutorial.rest](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) file. See [Quickstart: Full-text search](search-get-started-text.md) if you need help with the REST client.
+1. Start Visual Studio Code and open the [skillset-tutorial.rest](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) file. See [Quickstart: Full-text search](../../search-get-started-text.md) if you need help with the REST client.
 
 1. Provide values for the variables: search service endpoint, search service admin API key, an index name, a connection string to your Azure Storage account, and the blob container name.
 
@@ -295,19 +288,19 @@ POST {{baseUrl}}/skillsets?api-version=2024-07-01  HTTP/1.1
 
    | Skill                 | Description    |
    |-----------------------|----------------|
-   | [Optical Character Recognition](cognitive-search-skill-ocr.md) | Recognizes text and numbers in image files. |
-   | [Text Merge](cognitive-search-skill-textmerger.md)  | Creates "merged content" that recombines previously separated content, useful for documents with embedded images (PDF, DOCX, and so forth). Images and text are separated during the document cracking phase. The merge skill recombines them by inserting any recognized text, image captions, or tags created during enrichment into the same location where the image was extracted from in the document. </p>When you're working with merged content in a skillset, this node is inclusive of all text in the document, including text-only documents that never undergo OCR or image analysis. |
-   | [Language Detection](cognitive-search-skill-language-detection.md) | Detects the language and outputs either a language name or code. In multilingual data sets, a language field can be useful for filters. |
-   | [Entity Recognition](cognitive-search-skill-entity-recognition-v3.md) | Extracts the names of people, organizations, and locations from merged content. |
-   | [Text Split](cognitive-search-skill-textsplit.md)  | Breaks large merged content into smaller chunks before calling the key phrase extraction skill. Key phrase extraction accepts inputs of 50,000 characters or less. A few of the sample files need splitting up to fit within this limit. |
-   | [Key Phrase Extraction](cognitive-search-skill-keyphrases.md) | Pulls out the top key phrases.|
+   | [Optical Character Recognition](../../cognitive-search-skill-ocr.md) | Recognizes text and numbers in image files. |
+   | [Text Merge](../../cognitive-search-skill-textmerger.md)  | Creates "merged content" that recombines previously separated content, useful for documents with embedded images (PDF, DOCX, and so forth). Images and text are separated during the document cracking phase. The merge skill recombines them by inserting any recognized text, image captions, or tags created during enrichment into the same location where the image was extracted from in the document. </p>When you're working with merged content in a skillset, this node is inclusive of all text in the document, including text-only documents that never undergo OCR or image analysis. |
+   | [Language Detection](../../cognitive-search-skill-language-detection.md) | Detects the language and outputs either a language name or code. In multilingual data sets, a language field can be useful for filters. |
+   | [Entity Recognition](../../cognitive-search-skill-entity-recognition-v3.md) | Extracts the names of people, organizations, and locations from merged content. |
+   | [Text Split](../../cognitive-search-skill-textsplit.md)  | Breaks large merged content into smaller chunks before calling the key phrase extraction skill. Key phrase extraction accepts inputs of 50,000 characters or less. A few of the sample files need splitting up to fit within this limit. |
+   | [Key Phrase Extraction](../../cognitive-search-skill-keyphrases.md) | Pulls out the top key phrases.|
 
 + Each skill executes on the content of the document. During processing, Azure AI Search cracks each document to read content from different file formats. Found text originating in the source file is placed into a generated `content` field, one for each document. As such, the input becomes `"/document/content"`.
 
 + For key phrase extraction, because we use the text splitter skill to break larger files into pages, the context for the key phrase extraction skill is `"document/pages/*"` (for each page in the document) instead of `"/document/content"`.
 
 > [!NOTE]
-> Outputs can be mapped to an index, used as input to a downstream skill, or both as is the case with language code. In the index, a language code is useful for filtering. For more information about skillset fundamentals, see [How to define a skillset](cognitive-search-defining-skillset.md).
+> Outputs can be mapped to an index, used as input to a downstream skill, or both as is the case with language code. In the index, a language code is useful for filtering. For more information about skillset fundamentals, see [How to define a skillset](../../cognitive-search-defining-skillset.md).
 
 ### Step 3: Create an index
 
@@ -545,19 +538,19 @@ POST {{baseUrl}}/indexes/cog-search-demo-idx/docs/search?api-version=2024-07-01
   }
 ```
 
-These queries illustrate a few of the ways you can work with query syntax and filters on new fields created by Azure AI Search. For more query examples, see [Examples in Search Documents REST API](/rest/api/searchservice/documents/search-post#examples), [Simple syntax query examples](search-query-simple-examples.md), and [Full Lucene query examples](search-query-lucene-examples.md).
+These queries illustrate a few of the ways you can work with query syntax and filters on new fields created by Azure AI Search. For more query examples, see [Examples in Search Documents REST API](/rest/api/searchservice/documents/search-post#examples), [Simple syntax query examples](../../search-query-simple-examples.md), and [Full Lucene query examples](../../search-query-lucene-examples.md).
 
 <a name="reset"></a>
 
 ## Reset and rerun
 
-During early stages of development, iteration over the design is common. [Reset and rerun](search-howto-run-reset-indexers.md) helps with iteration.
+During early stages of development, iteration over the design is common. [Reset and rerun](../../search-howto-run-reset-indexers.md) helps with iteration.
 
 ## Takeaways
 
 This tutorial demonstrates the basic steps for using the REST APIs to create an AI enrichment pipeline: a data source, skillset, index, and indexer.
 
-[Built-in skills](cognitive-search-predefined-skills.md) were introduced, along with skillset definition that shows the mechanics of chaining skills together through inputs and outputs. You also learned that `outputFieldMappings` in the indexer definition is required for routing enriched values from the pipeline into a searchable index on an Azure AI Search service.
+[Built-in skills](../../cognitive-search-predefined-skills.md) were introduced, along with skillset definition that shows the mechanics of chaining skills together through inputs and outputs. You also learned that `outputFieldMappings` in the indexer definition is required for routing enriched values from the pipeline into a searchable index on an Azure AI Search service.
 
 Finally, you learned how to test results and reset the system for further iterations. You learned that issuing queries against the index returns the output created by the enriched indexing pipeline.
 
@@ -572,4 +565,4 @@ You can find and manage resources in the Azure portal, using the All resources o
 Now that you're familiar with all of the objects in an AI enrichment pipeline, take a closer look at skillset definitions and individual skills.
 
 > [!div class="nextstepaction"]
-> [How to create a skillset](cognitive-search-defining-skillset.md)
+> [How to create a skillset](../../cognitive-search-defining-skillset.md)

@@ -56,7 +56,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. A
 | [quickstart-agentic-retrieval](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/quickstart-agentic-retrieval) | [Quickstart: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md) | Creates a knowledge agent in Azure AI Search to integrate LLM reasoning into query planning. |
 | [quickstart-semantic-search](https://github.com/Azure-Samples/azure-search-dotnet-samples/blob/main/quickstart-semantic-search/) | [Quickstart: Semantic ranking using the Azure SDKs](search-get-started-semantic.md) | Shows the index schema and query request for invoking semantic ranker. |
 | [search-website](https://github.com/Azure-Samples/azure-search-static-web-app) | [Tutorial: Add search to web apps](tutorial-csharp-overview.md) | Demonstrates an end-to-end search app that includes bulk upload using the push APIs and a rich client for hosting the app and handling search requests.|
-| [tutorial-ai-enrichment](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/tutorial-ai-enrichment)  | [Tutorial: AI-generated searchable content from Azure blobs](cognitive-search-tutorial-blob-dotnet.md) | Shows how to configure an indexer and skillset. |
+| [tutorial-ai-enrichment](https://github.com/Azure-Samples/azure-search-dotnet-samples/tree/main/tutorial-ai-enrichment)  | [Tutorial: AI-generated searchable content from Azure blobs](tutorial-skillset.md) | Shows how to configure an indexer and skillset. |
 | [multiple-data-sources](https://github.com/Azure-Samples/azure-search-dotnet-scale/tree/main/multiple-data-sources)  | [Tutorial: Index from multiple data sources](tutorial-multiple-data-sources.md) | Merges content from two data sources into one search index. |
 | [Optimize-data-indexing](https://github.com/Azure-Samples/azure-search-dotnet-scale/tree/main/optimize-data-indexing) | [Tutorial: Optimize indexing with the push API](tutorial-optimize-indexing-push-api.md) | Demonstrates optimization techniques for pushing data into a search index. |
 | [DotNetHowTo](https://github.com/Azure-Samples/search-dotnet-getting-started/tree/master/DotNetHowTo)  | [How to use the .NET client library](search-howto-dotnet-sdk.md) | Steps through the basic workflow, but in more detail and with discussion of API usage.  |

@@ -30,7 +30,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. M
 | [quickstart](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart) | Source code for the REST portion of [Quickstart: Full-text search](search-get-started-text.md). This sample covers the basic workflow for creating, loading, and querying a search index using sample data. |
 | [quickstart-vectors](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-vectors) | Source code for [Quickstart: Vector search using REST APIs](search-get-started-vector.md). This sample covers the basic workflow for indexing and querying vector data. |
 | [quickstart-agentic-retrieval](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Quickstart-agentic-retrieval) | Source code for the REST portion of [Quickstart: Run agentic retrieval in Azure AI Search](search-get-started-agentic-retrieval.md). This sample shows you how to create a knowledge agent in Azure AI Search to integrate LLM reasoning into query planning. |
-| [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | Source code for [Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md). This sample shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
+| [skillset-tutorial](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skillset-tutorial) | Source code for [Tutorial: Use REST and AI to generate searchable content from Azure blobs](tutorial-skillset.md). This sample shows you how to create a skillset that iterates over Azure blobs to extract information and infer structure.|
 | [skill examples](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/skill-examples) | Skillset examples in indexer pipelines that include indexes and indexers so that you can follow field mappings, output field mappings, and source paths. |
 | [debug-sessions](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/Debug-sessions) | Source code for [Tutorial: Diagnose, repair, and commit changes to your skillset](cognitive-search-tutorial-debug-sessions.md). This sample shows you how to use a skillset debug session in the Azure portal. REST is used to create the objects used during debug.|
 | [custom-analyzers](https://github.com/Azure-Samples/azure-search-rest-samples/tree/main/custom-analyzers) | Source code for [Tutorial: Create a custom analyzer for phone numbers](tutorial-create-custom-analyzer.md). This sample explains how to use analyzers to preserve patterns and special characters in searchable content.|

@@ -195,4 +195,4 @@ If you used a free service, remember that you're limited to three indexes, index
 You can create skillsets using the Azure portal, .NET SDK, or REST API. To further your knowledge, try the REST API by using a REST client and more sample data:
 
 > [!div class="nextstepaction"]
-> [Tutorial: Use skillsets to generate searchable content in Azure AI Search](cognitive-search-tutorial-blob.md)
+> [Tutorial: Use skillsets to generate searchable content in Azure AI Search](tutorial-skillset.md)

@@ -174,7 +174,7 @@ To verify whether the cache is operational, modify a skillset and run the indexe
 
 Skillsets that include image analysis and Optical Character Recognition (OCR) of scanned documents make good test cases. If you modify a downstream text skill or any skill that isn't image-related, the indexer can retrieve all of the previously processed image and OCR content from cache, updating and processing only the text-related changes indicated by your edits.  You can expect to see fewer documents in the indexer execution document count, shorter execution times, and fewer charges on your bill. 
 
-The [file set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) used in [cog-search-demo tutorials](cognitive-search-tutorial-blob.md) is a useful test case because it contains 14 files of various formats JPG, PNG, HTML, DOCX, PPTX, and other types. Change `en` to `es` or another language in the text translation skill for proof-of-concept testing of incremental enrichment.
+The [file set](https://github.com/Azure-Samples/azure-search-sample-data/tree/main/ai-enrichment-mixed-media) used in [cog-search-demo tutorials](tutorial-skillset.md) is a useful test case because it contains 14 files of various formats JPG, PNG, HTML, DOCX, PPTX, and other types. Change `en` to `es` or another language in the text translation skill for proof-of-concept testing of incremental enrichment.
 
 ## Common errors
 
@@ -191,4 +191,4 @@ Incremental enrichment is applicable on indexers that contain skillsets, providi
 + [Incremental enrichment (lifecycle and management)](cognitive-search-incremental-indexing-conceptual.md)
 + [Skillset concepts and composition](cognitive-search-working-with-skillsets.md)
 + [Create a skillset](cognitive-search-defining-skillset.md)
-+ [Tutorial: Use REST and AI to generate searchable content from Azure blobs](cognitive-search-tutorial-blob.md)
++ [Tutorial: Use REST and AI to generate searchable content from Azure blobs](tutorial-skillset.md)

@@ -327,7 +327,7 @@ Deleting a document doesn't immediately free up space in the index. Every few mi
 
 1. Check the values of the document key field: `search=*&$select=HotelId`. A simple string is straightforward, but if the index uses a base-64 encoded field, or if search documents were generated from a `parsingMode` setting, you might be working with values that you aren't familiar with.
 
-1. [Look up the document](/rest/api/searchservice/documents/get) to verify the value of the document ID and to review its content before deleting it. Specify the key or document ID in the request. The following examples illustrate a simple string for the [Hotels sample index](search-get-started-portal.md) and a base-64 encoded string for the metadata_storage_path key of the [cog-search-demo index](cognitive-search-tutorial-blob.md).
+1. [Look up the document](/rest/api/searchservice/documents/get) to verify the value of the document ID and to review its content before deleting it. Specify the key or document ID in the request. The following examples illustrate a simple string for the [Hotels sample index](search-get-started-portal.md) and a base-64 encoded string for the metadata_storage_path key of the [cog-search-demo index](tutorial-skillset.md).
 
     ```http
     GET https://[service name].search.windows.net/indexes/hotel-sample-index/docs/1111?api-version=2024-07-01

@@ -180,10 +180,8 @@ items:
       href: tutorial-rag-build-solution-minimize-storage.md
   - name: Skills tutorials
     items:
-    - name: C#
-      href: cognitive-search-tutorial-blob-dotnet.md
-    - name: REST
-      href: cognitive-search-tutorial-blob.md
+    - name: Create a skillset
+      href: tutorial-skillset.md
     - name: Debug a skillset
       href: cognitive-search-tutorial-debug-sessions.md
 - name: How-to guides

@@ -0,0 +1,25 @@
+---
+title: "Tutorial: Skillsets"
+titleSuffix: Azure AI Search
+description: Learn how to create a skillset that calls built-in skills to enrich content during indexing.
+author: heidisteen
+ms.author: heidist
+ms.service: azure-ai-search
+ms.topic: tutorial
+ms.date: 7/11/2025
+zone_pivot_groups: tutorial-create-skillset
+---
+
+# Quickstart: Skillsets in Azure AI Search
+
+::: zone pivot="csharp"
+
+[!INCLUDE [C# quickstart](includes/tutorials/skillset-csharp.md)]
+
+::: zone-end
+
+::: zone pivot="rest"
+
+[!INCLUDE [REST quickstart](includes/tutorials/skillset-rest.md)]
+
+::: zone-end