[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7180MT757FN89KPJWRGYXGP":3,"skill-same:01M7180MT757FN89KPJWRGYXGP":65},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":45,"commitSha":46,"skillDirectory":47,"forksCount":14,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M7180MT757FN89KPJWRGYXGP","azure-search-documents-dotnet",null,"Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fglobal\u002Fazure-search-documents-dotnet\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"dotnet","documents","azure","azure search documents dotnet","search","search documents","2026","sdk","hybrid","unknown","for","with","Hybrid Search","full","community","use","text","applications","vector","azure ai","2026-11-10 10:12:30",false,"---\nname: azure-search-documents-dotnet\ndescription: Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.\nrisk: unknown\nsource: community\ndate_added: '2026-02-27'\n---\n\n# Azure.Search.Documents (.NET)\n\nBuild search applications with full-text, vector, semantic, and hybrid search capabilities.\n\n## Installation\n\n```bash\ndotnet add package Azure.Search.Documents\ndotnet add package Azure.Identity\n```\n\n**Current Versions**: Stable v11.7.0, Preview v11.8.0-beta.1\n\n## Environment Variables\n\n```bash\nSEARCH_ENDPOINT=https:\u002F\u002F\u003Csearch-service>.search.windows.net\nSEARCH_INDEX_NAME=\u003Cindex-name>\n# For API key auth (not recommended for production)\nSEARCH_API_KEY=\u003Capi-key>\n```\n\n## Authentication\n\n**DefaultAzureCredential (preferred)**:\n```csharp\nusing Azure.Identity;\nusing Azure.Search.Documents;\n\nvar credential = new DefaultAzureCredential();\nvar client = new SearchClient(\n    new Uri(Environment.GetEnvironmentVariable(\"SEARCH_ENDPOINT\")),\n    Environment.GetEnvironmentVariable(\"SEARCH_INDEX_NAME\"),\n    credential);\n```\n\n**API Key**:\n```csharp\nusing Azure;\nusing Azure.Search.Documents;\n\nvar credential = new AzureKeyCredential(\n    Environment.GetEnvironmentVariable(\"SEARCH_API_KEY\"));\nvar client = new SearchClient(\n    new Uri(Environment.GetEnvironmentVariable(\"SEARCH_ENDPOINT\")),\n    Environment.GetEnvironmentVariable(\"SEARCH_INDEX_NAME\"),\n    credential);\n```\n\n## Client Selection\n\n| Client | Purpose |\n|--------|---------|\n| `SearchClient` | Query indexes, upload\u002Fupdate\u002Fdelete documents |\n| `SearchIndexClient` | Create\u002Fmanage indexes, synonym maps |\n| `SearchIndexerClient` | Manage indexers, skillsets, data sources |\n\n## Index Creation\n\n### Using FieldBuilder (Recommended)\n\n```csharp\nusing Azure.Search.Documents.Indexes;\nusing Azure.Search.Documents.Indexes.Models;\n\n\u002F\u002F Define model with attributes\npublic class Hotel\n{\n    [SimpleField(IsKey = true, IsFilterable = true)]\n    public string HotelId { get; set; }\n\n    [SearchableField(IsSortable = true)]\n    public string HotelName { get; set; }\n\n    [SearchableField(AnalyzerName = LexicalAnalyzerName.EnLucene)]\n    public string Description { get; set; }\n\n    [SimpleField(IsFilterable = true, IsSortable = true, IsFacetable = true)]\n    public double? Rating { get; set; }\n\n    [VectorSearchField(VectorSearchDimensions = 1536, VectorSearchProfileName = \"vector-profile\")]\n    public ReadOnlyMemory\u003Cfloat>? DescriptionVector { get; set; }\n}\n\n\u002F\u002F Create index\nvar indexClient = new SearchIndexClient(endpoint, credential);\nvar fieldBuilder = new FieldBuilder();\nvar fields = fieldBuilder.Build(typeof(Hotel));\n\nvar index = new SearchIndex(\"hotels\")\n{\n    Fields = fields,\n    VectorSearch = new VectorSearch\n    {\n        Profiles = { new VectorSearchProfile(\"vector-profile\", \"hnsw-algo\") },\n        Algorithms = { new HnswAlgorithmConfiguration(\"hnsw-algo\") }\n    }\n};\n\nawait indexClient.CreateOrUpdateIndexAsync(index);\n```\n\n### Manual Field Definition\n\n```csharp\nvar index = new SearchIndex(\"hotels\")\n{\n    Fields =\n    {\n        new SimpleField(\"hotelId\", SearchFieldDataType.String) { IsKey = true, IsFilterable = true },\n        new SearchableField(\"hotelName\") { IsSortable = true },\n        new SearchableField(\"description\") { AnalyzerName = LexicalAnalyzerName.EnLucene },\n        new SimpleField(\"rating\", SearchFieldDataType.Double) { IsFilterable = true, IsSortable = true },\n        new SearchField(\"descriptionVector\", SearchFieldDataType.Collection(SearchFieldDataType.Single))\n        {\n            VectorSearchDimensions = 1536,\n            VectorSearchProfileName = \"vector-profile\"\n        }\n    }\n};\n```\n\n## Document Operations\n\n```csharp\nvar searchClient = new SearchClient(endpoint, indexName, credential);\n\n\u002F\u002F Upload (add new)\nvar hotels = new[] { new Hotel { HotelId = \"1\", HotelName = \"Hotel A\" } };\nawait searchClient.UploadDocumentsAsync(hotels);\n\n\u002F\u002F Merge (update existing)\nawait searchClient.MergeDocumentsAsync(hotels);\n\n\u002F\u002F Merge or Upload (upsert)\nawait searchClient.MergeOrUploadDocumentsAsync(hotels);\n\n\u002F\u002F Delete\nawait searchClient.DeleteDocumentsAsync(\"hotelId\", new[] { \"1\", \"2\" });\n\n\u002F\u002F Batch operations\nvar batch = IndexDocumentsBatch.Create(\n    IndexDocumentsAction.Upload(hotel1),\n    IndexDocumentsAction.Merge(hotel2),\n    IndexDocumentsAction.Delete(hotel3));\nawait searchClient.IndexDocumentsAsync(batch);\n```\n\n## Search Patterns\n\n### Basic Search\n\n```csharp\nvar options = new SearchOptions\n{\n    Filter = \"rating ge 4\",\n    OrderBy = { \"rating desc\" },\n    Select = { \"hotelId\", \"hotelName\", \"rating\" },\n    Size = 10,\n    Skip = 0,\n    IncludeTotalCount = true\n};\n\nSearchResults\u003CHotel> results = await searchClient.SearchAsync\u003CHotel>(\"luxury\", options);\n\nConsole.WriteLine($\"Total: {results.TotalCount}\");\nawait foreach (SearchResult\u003CHotel> result in results.GetResultsAsync())\n{\n    Console.WriteLine($\"{result.Document.HotelName} (Score: {result.Score})\");\n}\n```\n\n### Faceted Search\n\n```csharp\nvar options = new SearchOptions\n{\n    Facets = { \"rating,count:5\", \"category\" }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(\"*\", options);\n\nforeach (var facet in results.Value.Facets[\"rating\"])\n{\n    Console.WriteLine($\"Rating {facet.Value}: {facet.Count}\");\n}\n```\n\n### Autocomplete and Suggestions\n\n```csharp\n\u002F\u002F Autocomplete\nvar autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };\nvar autocomplete = await searchClient.AutocompleteAsync(\"lux\", \"suggester-name\", autocompleteOptions);\n\n\u002F\u002F Suggestions\nvar suggestOptions = new SuggestOptions { UseFuzzyMatching = true };\nvar suggestions = await searchClient.SuggestAsync\u003CHotel>(\"lux\", \"suggester-name\", suggestOptions);\n```\n\n## Vector Search\n\nSee references\u002Fvector-search.md for detailed patterns.\n\n```csharp\nusing Azure.Search.Documents.Models;\n\n\u002F\u002F Pure vector search\nvar vectorQuery = new VectorizedQuery(embedding)\n{\n    KNearestNeighborsCount = 5,\n    Fields = { \"descriptionVector\" }\n};\n\nvar options = new SearchOptions\n{\n    VectorSearch = new VectorSearchOptions\n    {\n        Queries = { vectorQuery }\n    }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(null, options);\n```\n\n## Semantic Search\n\nSee references\u002Fsemantic-search.md for detailed patterns.\n\n```csharp\nvar options = new SearchOptions\n{\n    QueryType = SearchQueryType.Semantic,\n    SemanticSearch = new SemanticSearchOptions\n    {\n        SemanticConfigurationName = \"my-semantic-config\",\n        QueryCaption = new QueryCaption(QueryCaptionType.Extractive),\n        QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)\n    }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(\"best hotel for families\", options);\n\n\u002F\u002F Access semantic answers\nforeach (var answer in results.Value.SemanticSearch.Answers)\n{\n    Console.WriteLine($\"Answer: {answer.Text} (Score: {answer.Score})\");\n}\n\n\u002F\u002F Access captions\nawait foreach (var result in results.Value.GetResultsAsync())\n{\n    var caption = result.SemanticSearch?.Captions?.FirstOrDefault();\n    Console.WriteLine($\"Caption: {caption?.Text}\");\n}\n```\n\n## Hybrid Search (Vector + Keyword + Semantic)\n\n```csharp\nvar vectorQuery = new VectorizedQuery(embedding)\n{\n    KNearestNeighborsCount = 5,\n    Fields = { \"descriptionVector\" }\n};\n\nvar options = new SearchOptions\n{\n    QueryType = SearchQueryType.Semantic,\n    SemanticSearch = new SemanticSearchOptions\n    {\n        SemanticConfigurationName = \"my-semantic-config\"\n    },\n    VectorSearch = new VectorSearchOptions\n    {\n        Queries = { vectorQuery }\n    }\n};\n\n\u002F\u002F Combines keyword search, vector search, and semantic ranking\nvar results = await searchClient.SearchAsync\u003CHotel>(\"luxury beachfront\", options);\n```\n\n## Field Attributes Reference\n\n| Attribute | Purpose |\n|-----------|---------|\n| `SimpleField` | Non-searchable field (filters, sorting, facets) |\n| `SearchableField` | Full-text searchable field |\n| `VectorSearchField` | Vector embedding field |\n| `IsKey = true` | Document key (required, one per index) |\n| `IsFilterable = true` | Enable $filter expressions |\n| `IsSortable = true` | Enable $orderby |\n| `IsFacetable = true` | Enable faceted navigation |\n| `IsHidden = true` | Exclude from results |\n| `AnalyzerName` | Specify text analyzer |\n\n## Error Handling\n\n```csharp\nusing Azure;\n\ntry\n{\n    var results = await searchClient.SearchAsync\u003CHotel>(\"query\");\n}\ncatch (RequestFailedException ex) when (ex.Status == 404)\n{\n    Console.WriteLine(\"Index not found\");\n}\ncatch (RequestFailedException ex)\n{\n    Console.WriteLine($\"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}\");\n}\n```\n\n## Best Practices\n\n1. **Use `DefaultAzureCredential`** over API keys for production\n2. **Use `FieldBuilder`** with model attributes for type-safe index definitions\n3. **Use `CreateOrUpdateIndexAsync`** for idempotent index creation\n4. **Batch document operations** for better throughput\n5. **Use `Select`** to return only needed fields\n6. **Configure semantic search** for natural language queries\n7. **Combine vector + keyword + semantic** for best relevance\n\n## Reference Files\n\n| File | Contents |\n|------|----------|\n| references\u002Fvector-search.md | Vector search, hybrid search, vectorizers |\n| references\u002Fsemantic-search.md | Semantic ranking, captions, answers |\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n","# Azure.Search.Documents (.NET)\n\nBuild search applications with full-text, vector, semantic, and hybrid search capabilities.\n\n## Installation\n\n```bash\ndotnet add package Azure.Search.Documents\ndotnet add package Azure.Identity\n```\n\n**Current Versions**: Stable v11.7.0, Preview v11.8.0-beta.1\n\n## Environment Variables\n\n```bash\nSEARCH_ENDPOINT=https:\u002F\u002F\u003Csearch-service>.search.windows.net\nSEARCH_INDEX_NAME=\u003Cindex-name>\n# For API key auth (not recommended for production)\nSEARCH_API_KEY=\u003Capi-key>\n```\n\n## Authentication\n\n**DefaultAzureCredential (preferred)**:\n```csharp\nusing Azure.Identity;\nusing Azure.Search.Documents;\n\nvar credential = new DefaultAzureCredential();\nvar client = new SearchClient(\n    new Uri(Environment.GetEnvironmentVariable(\"SEARCH_ENDPOINT\")),\n    Environment.GetEnvironmentVariable(\"SEARCH_INDEX_NAME\"),\n    credential);\n```\n\n**API Key**:\n```csharp\nusing Azure;\nusing Azure.Search.Documents;\n\nvar credential = new AzureKeyCredential(\n    Environment.GetEnvironmentVariable(\"SEARCH_API_KEY\"));\nvar client = new SearchClient(\n    new Uri(Environment.GetEnvironmentVariable(\"SEARCH_ENDPOINT\")),\n    Environment.GetEnvironmentVariable(\"SEARCH_INDEX_NAME\"),\n    credential);\n```\n\n## Client Selection\n\n| Client | Purpose |\n|--------|---------|\n| `SearchClient` | Query indexes, upload\u002Fupdate\u002Fdelete documents |\n| `SearchIndexClient` | Create\u002Fmanage indexes, synonym maps |\n| `SearchIndexerClient` | Manage indexers, skillsets, data sources |\n\n## Index Creation\n\n### Using FieldBuilder (Recommended)\n\n```csharp\nusing Azure.Search.Documents.Indexes;\nusing Azure.Search.Documents.Indexes.Models;\n\n\u002F\u002F Define model with attributes\npublic class Hotel\n{\n    [SimpleField(IsKey = true, IsFilterable = true)]\n    public string HotelId { get; set; }\n\n    [SearchableField(IsSortable = true)]\n    public string HotelName { get; set; }\n\n    [SearchableField(AnalyzerName = LexicalAnalyzerName.EnLucene)]\n    public string Description { get; set; }\n\n    [SimpleField(IsFilterable = true, IsSortable = true, IsFacetable = true)]\n    public double? Rating { get; set; }\n\n    [VectorSearchField(VectorSearchDimensions = 1536, VectorSearchProfileName = \"vector-profile\")]\n    public ReadOnlyMemory\u003Cfloat>? DescriptionVector { get; set; }\n}\n\n\u002F\u002F Create index\nvar indexClient = new SearchIndexClient(endpoint, credential);\nvar fieldBuilder = new FieldBuilder();\nvar fields = fieldBuilder.Build(typeof(Hotel));\n\nvar index = new SearchIndex(\"hotels\")\n{\n    Fields = fields,\n    VectorSearch = new VectorSearch\n    {\n        Profiles = { new VectorSearchProfile(\"vector-profile\", \"hnsw-algo\") },\n        Algorithms = { new HnswAlgorithmConfiguration(\"hnsw-algo\") }\n    }\n};\n\nawait indexClient.CreateOrUpdateIndexAsync(index);\n```\n\n### Manual Field Definition\n\n```csharp\nvar index = new SearchIndex(\"hotels\")\n{\n    Fields =\n    {\n        new SimpleField(\"hotelId\", SearchFieldDataType.String) { IsKey = true, IsFilterable = true },\n        new SearchableField(\"hotelName\") { IsSortable = true },\n        new SearchableField(\"description\") { AnalyzerName = LexicalAnalyzerName.EnLucene },\n        new SimpleField(\"rating\", SearchFieldDataType.Double) { IsFilterable = true, IsSortable = true },\n        new SearchField(\"descriptionVector\", SearchFieldDataType.Collection(SearchFieldDataType.Single))\n        {\n            VectorSearchDimensions = 1536,\n            VectorSearchProfileName = \"vector-profile\"\n        }\n    }\n};\n```\n\n## Document Operations\n\n```csharp\nvar searchClient = new SearchClient(endpoint, indexName, credential);\n\n\u002F\u002F Upload (add new)\nvar hotels = new[] { new Hotel { HotelId = \"1\", HotelName = \"Hotel A\" } };\nawait searchClient.UploadDocumentsAsync(hotels);\n\n\u002F\u002F Merge (update existing)\nawait searchClient.MergeDocumentsAsync(hotels);\n\n\u002F\u002F Merge or Upload (upsert)\nawait searchClient.MergeOrUploadDocumentsAsync(hotels);\n\n\u002F\u002F Delete\nawait searchClient.DeleteDocumentsAsync(\"hotelId\", new[] { \"1\", \"2\" });\n\n\u002F\u002F Batch operations\nvar batch = IndexDocumentsBatch.Create(\n    IndexDocumentsAction.Upload(hotel1),\n    IndexDocumentsAction.Merge(hotel2),\n    IndexDocumentsAction.Delete(hotel3));\nawait searchClient.IndexDocumentsAsync(batch);\n```\n\n## Search Patterns\n\n### Basic Search\n\n```csharp\nvar options = new SearchOptions\n{\n    Filter = \"rating ge 4\",\n    OrderBy = { \"rating desc\" },\n    Select = { \"hotelId\", \"hotelName\", \"rating\" },\n    Size = 10,\n    Skip = 0,\n    IncludeTotalCount = true\n};\n\nSearchResults\u003CHotel> results = await searchClient.SearchAsync\u003CHotel>(\"luxury\", options);\n\nConsole.WriteLine($\"Total: {results.TotalCount}\");\nawait foreach (SearchResult\u003CHotel> result in results.GetResultsAsync())\n{\n    Console.WriteLine($\"{result.Document.HotelName} (Score: {result.Score})\");\n}\n```\n\n### Faceted Search\n\n```csharp\nvar options = new SearchOptions\n{\n    Facets = { \"rating,count:5\", \"category\" }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(\"*\", options);\n\nforeach (var facet in results.Value.Facets[\"rating\"])\n{\n    Console.WriteLine($\"Rating {facet.Value}: {facet.Count}\");\n}\n```\n\n### Autocomplete and Suggestions\n\n```csharp\n\u002F\u002F Autocomplete\nvar autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };\nvar autocomplete = await searchClient.AutocompleteAsync(\"lux\", \"suggester-name\", autocompleteOptions);\n\n\u002F\u002F Suggestions\nvar suggestOptions = new SuggestOptions { UseFuzzyMatching = true };\nvar suggestions = await searchClient.SuggestAsync\u003CHotel>(\"lux\", \"suggester-name\", suggestOptions);\n```\n\n## Vector Search\n\nSee references\u002Fvector-search.md for detailed patterns.\n\n```csharp\nusing Azure.Search.Documents.Models;\n\n\u002F\u002F Pure vector search\nvar vectorQuery = new VectorizedQuery(embedding)\n{\n    KNearestNeighborsCount = 5,\n    Fields = { \"descriptionVector\" }\n};\n\nvar options = new SearchOptions\n{\n    VectorSearch = new VectorSearchOptions\n    {\n        Queries = { vectorQuery }\n    }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(null, options);\n```\n\n## Semantic Search\n\nSee references\u002Fsemantic-search.md for detailed patterns.\n\n```csharp\nvar options = new SearchOptions\n{\n    QueryType = SearchQueryType.Semantic,\n    SemanticSearch = new SemanticSearchOptions\n    {\n        SemanticConfigurationName = \"my-semantic-config\",\n        QueryCaption = new QueryCaption(QueryCaptionType.Extractive),\n        QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)\n    }\n};\n\nvar results = await searchClient.SearchAsync\u003CHotel>(\"best hotel for families\", options);\n\n\u002F\u002F Access semantic answers\nforeach (var answer in results.Value.SemanticSearch.Answers)\n{\n    Console.WriteLine($\"Answer: {answer.Text} (Score: {answer.Score})\");\n}\n\n\u002F\u002F Access captions\nawait foreach (var result in results.Value.GetResultsAsync())\n{\n    var caption = result.SemanticSearch?.Captions?.FirstOrDefault();\n    Console.WriteLine($\"Caption: {caption?.Text}\");\n}\n```\n\n## Hybrid Search (Vector + Keyword + Semantic)\n\n```csharp\nvar vectorQuery = new VectorizedQuery(embedding)\n{\n    KNearestNeighborsCount = 5,\n    Fields = { \"descriptionVector\" }\n};\n\nvar options = new SearchOptions\n{\n    QueryType = SearchQueryType.Semantic,\n    SemanticSearch = new SemanticSearchOptions\n    {\n        SemanticConfigurationName = \"my-semantic-config\"\n    },\n    VectorSearch = new VectorSearchOptions\n    {\n        Queries = { vectorQuery }\n    }\n};\n\n\u002F\u002F Combines keyword search, vector search, and semantic ranking\nvar results = await searchClient.SearchAsync\u003CHotel>(\"luxury beachfront\", options);\n```\n\n## Field Attributes Reference\n\n| Attribute | Purpose |\n|-----------|---------|\n| `SimpleField` | Non-searchable field (filters, sorting, facets) |\n| `SearchableField` | Full-text searchable field |\n| `VectorSearchField` | Vector embedding field |\n| `IsKey = true` | Document key (required, one per index) |\n| `IsFilterable = true` | Enable $filter expressions |\n| `IsSortable = true` | Enable $orderby |\n| `IsFacetable = true` | Enable faceted navigation |\n| `IsHidden = true` | Exclude from results |\n| `AnalyzerName` | Specify text analyzer |\n\n## Error Handling\n\n```csharp\nusing Azure;\n\ntry\n{\n    var results = await searchClient.SearchAsync\u003CHotel>(\"query\");\n}\ncatch (RequestFailedException ex) when (ex.Status == 404)\n{\n    Console.WriteLine(\"Index not found\");\n}\ncatch (RequestFailedException ex)\n{\n    Console.WriteLine($\"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}\");\n}\n```\n\n## Best Practices\n\n1. **Use `DefaultAzureCredential`** over API keys for production\n2. **Use `FieldBuilder`** with model attributes for type-safe index definitions\n3. **Use `CreateOrUpdateIndexAsync`** for idempotent index creation\n4. **Batch document operations** for better throughput\n5. **Use `Select`** to return only needed fields\n6. **Configure semantic search** for natural language queries\n7. **Combine vector + keyword + semantic** for best relevance\n\n## Reference Files\n\n| File | Contents |\n|------|----------|\n| references\u002Fvector-search.md | Vector search, hybrid search, vectorizers |\n| references\u002Fsemantic-search.md | Semantic ranking, captions, answers |\n\n## When to Use\nThis skill is applicable to execute the workflow or actions described in the overview.\n",{"name":5,"risk":28,"source":33,"date_added":44,"description":7},"2026-02-27","main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fglobal\u002Fazure-search-documents-dotnet","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills 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Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[95,96,97,98,99,100,23,101,102,103,104,105,106,107,108,109,110,111,112,113],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","when","brew","live","data","google","install","read","across","api",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":39},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[121,122,95,106,107,108,109,99,23,111,123,112,113,105,103,102,101,110,100,98],"trend radar","radar","pulse",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":39},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[131,132,133,134,135,136],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":148,"publishedAt":39},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[143],{"categoryNo":144,"categoryName":145,"taxonomyType":146,"taxonomyTypeDesc":147},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[139,85,149,150,151,30,106,152,153,154,97,155,110],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"publishedAt":39},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[158,163,164,165,110,166,153,167,168,169,170,171],"status","play","go","latest","github","discover","control","cmd","volume"]