Azure.Search.Documents (.NET) workflow skill. Use this skill when the user needs Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
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Azure.Search.Documents (.NET) workflow skill. Use this skill when the user needs Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/azure-search-documents-dotnet 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.Search.Documents (.NET) Build search applications with full-text, vector, semantic, and hybrid search capabilities.
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, Client Selection, Document Operations, Search Patterns, Vector Search.
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 Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.
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.
Current Versions: Stable v11.7.0, Preview v11.8.0-beta.1
Imported: Environment Variables
SEARCH_ENDPOINT=https://<search-service>.search.windows.net
SEARCH_INDEX_NAME=<index-name>
# For API key auth (not recommended for production)
SEARCH_API_KEY=<api-key>
Examples
Example 1: Ask for the upstream workflow directly
Use @azure-search-documents-dotnet 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-search-documents-dotnet 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-search-documents-dotnet 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-search-documents-dotnet 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 DefaultAzureCredential over API keys for production
Use FieldBuilder with model attributes for type-safe index definitions
Use CreateOrUpdateIndexAsync for idempotent index creation
Batch document operations for better throughput
Use Select to return only needed fields
Configure semantic search for natural language queries
Combine vector + keyword + semantic for best relevance
Imported Operating Notes
Imported: Best Practices
Use DefaultAzureCredential over API keys for production
Use FieldBuilder with model attributes for type-safe index definitions
Use CreateOrUpdateIndexAsync for idempotent index creation
Batch document operations for better throughput
Use Select to return only needed fields
Configure semantic search for natural language queries
Combine vector + keyword + semantic for best relevance
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-search-documents-dotnet, 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: Index Creation
Using FieldBuilder (Recommended)
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
// Define model with attributespublicclassHotel
{
[SimpleField(IsKey = true, IsFilterable = true)]
publicstring HotelId { get; set; }
[SearchableField(IsSortable = true)]
publicstring HotelName { get; set; }
[SearchableField(AnalyzerName = LexicalAnalyzerName.EnLucene)]
publicstring Description { get; set; }
[SimpleField(IsFilterable = true, IsSortable = true, IsFacetable = true)]
publicdouble? Rating { get; set; }
[VectorSearchField(VectorSearchDimensions = 1536, VectorSearchProfileName = "vector-profile")]
public ReadOnlyMemory<float>? DescriptionVector { get; set; }
}
// Create indexvar indexClient = new SearchIndexClient(endpoint, credential);
var fieldBuilder = new FieldBuilder();
var fields = fieldBuilder.Build(typeof(Hotel));
var index = new SearchIndex("hotels")
{
Fields = fields,
VectorSearch = new VectorSearch
{
Profiles = { new VectorSearchProfile("vector-profile", "hnsw-algo") },
Algorithms = { new HnswAlgorithmConfiguration("hnsw-algo") }
}
};
await indexClient.CreateOrUpdateIndexAsync(index);
Manual Field Definition
var index = new SearchIndex("hotels")
{
Fields =
{
new SimpleField("hotelId", SearchFieldDataType.String) { IsKey = true, IsFilterable = true },
new SearchableField("hotelName") { IsSortable = true },
new SearchableField("description") { AnalyzerName = LexicalAnalyzerName.EnLucene },
new SimpleField("rating", SearchFieldDataType.Double) { IsFilterable = true, IsSortable = true },
new SearchField("descriptionVector", SearchFieldDataType.Collection(SearchFieldDataType.Single))
{
VectorSearchDimensions = 1536,
VectorSearchProfileName = "vector-profile"
}
}
};
Imported: Field Attributes Reference
Attribute
Purpose
SimpleField
Non-searchable field (filters, sorting, facets)
SearchableField
Full-text searchable field
VectorSearchField
Vector embedding field
IsKey = true
Document key (required, one per index)
IsFilterable = true
Enable $filter expressions
IsSortable = true
Enable $orderby
IsFacetable = true
Enable faceted navigation
IsHidden = true
Exclude from results
AnalyzerName
Specify text analyzer
Imported: Reference Files
File
Contents
references/vector-search.md
Vector search, hybrid search, vectorizers
references/semantic-search.md
Semantic ranking, captions, answers
Imported: Authentication
DefaultAzureCredential (preferred):
using Azure.Identity;
using Azure.Search.Documents;
var credential = new DefaultAzureCredential();
var client = new SearchClient(
new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
credential);
API Key:
using Azure;
using Azure.Search.Documents;
var credential = new AzureKeyCredential(
Environment.GetEnvironmentVariable("SEARCH_API_KEY"));
var client = new SearchClient(
new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
credential);
