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Skills azure-search-documents-ts
📦
azure-search-documents-ts
Content revision r2 Safe ⚙️ External commands🌐 Network access🔑 Env variables
Build Azure AI Search Apps with TypeScript
Azure AI Search integrations require careful SDK configuration across indexing, authentication, and multiple query modes. This skill provides focused TypeScript patterns for building and reviewing those workflows.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "azure-search-documents-ts" from https://skillstore.io/skills/sickn33-azure-search-documents-ts.md and its manifest at https://skillstore.io/api/skills/sickn33-azure-search-documents-ts/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "azure-search-documents-ts". Create a search plan for a product catalog with category filters and vector similarity.
Expected outcome:
- Define searchable title and description fields.
- Add a filterable and facetable category field.
- Configure a vector field and matching search profile.
- Combine text and vector retrieval in the query.
- Return selected fields and limit the result count.
Using "azure-search-documents-ts". Explain how to update many documents without replacing the entire index.
Expected outcome:
Use batch indexing actions for uploads, merges, and deletes. Use incremental merge-or-upload operations when records may be new or existing.
Using "azure-search-documents-ts". Review a semantic search feature request.
Expected outcome:
- Confirm that the index has a semantic configuration.
- Choose prioritized title and content fields.
- Request extractive captions or answers only when needed.
- Validate service support and measure relevance before release.
Security Audit
Safev5 • 7/23/2026 Open versioned report
All 45 static findings are false positives. The command detections are Markdown fences, TypeScript template literals, or inline code, while the URL and environment access are normal configuration examples. No prompt injection, credential exfiltration, or hidden malicious intent was found in SKILL.md.
1
Files scanned
277
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚙️ External commands (36)
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🌐 Network access (1)
🔑 Env variables (4)
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/sickn33-azure-search-documents-ts/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/sickn33-azure-search-documents-ts?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/sickn33-azure-search-documents-ts/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/sickn33-azure-search-documents-ts.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). azure-search-documents-ts security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-azure-search-documents-ts/audits/5BibTeX citation
@techreport{sickn33-sickn33-azure-search-documents-ts-2026,
author = {sickn33},
title = {azure-search-documents-ts security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-azure-search-documents-ts/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "azure-search-documents-ts security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-azure-search-documents-ts/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-azure-search-documents-ts:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
68
Community
83
Spec Compliance
What You Can Build
Create a Search Prototype
Build a TypeScript prototype that indexes product records and supports text, vector, and hybrid queries.
Design Search Relevance
Compare semantic ranking, vector retrieval, filters, and facets for a search experience.
Review Azure Integration
Check SDK authentication, index definitions, batch operations, and query options before implementation.
Try These Prompts
Set Up a Client
Create a minimal TypeScript setup for Azure AI Search using DefaultAzureCredential. Use environment variables for the endpoint and index name.
Define and Populate an Index
Design a product index with searchable text, category facets, and a 1536-dimension vector field. Include a batch upload workflow.
Add Hybrid Search
Build a hybrid query that combines user text with an embedding. Return ten results and include selected document fields.
Plan a Production Search Flow
Review my Azure AI Search requirements. Propose semantic ranking, filters, facets, batching, validation, retry handling, and tests with clear assumptions.
Best Practices
- Prefer DefaultAzureCredential and least-privilege identities instead of embedding administrator keys in source code.
- Batch document changes and use incremental updates to reduce indexing overhead.
- Validate field names, filter values, vector dimensions, service capabilities, and query performance in the target environment.
Avoid
- Do not treat example placeholders, omitted types, or the undefined embedding function as production-ready code.
- Do not build authorization filters directly from untrusted client input.
- Do not request total counts, semantic features, or large nearest-neighbor sets without measuring cost and latency.
Frequently Asked Questions
Does this skill create an Azure AI Search service?
No. It assumes that a service exists and that the application has suitable access.
Which authentication method does the example use?
The main example uses DefaultAzureCredential from the Azure Identity package.
Does the skill generate embeddings?
No. Vector examples call a placeholder embedding function that you must implement or connect to a provider.
Can it demonstrate hybrid and semantic search?
Yes. It includes separate patterns for hybrid retrieval and semantic ranking.
Does it support document updates and deletes?
Yes. It shows uploads and a mixed batch containing upload, merge, and delete actions.
Are the examples production-ready?
No. Add validation, error handling, retries, security controls, observability, and tests for your environment.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/azure-search-documents-tsRef
f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
Maintenance freshness
7/26/2026
Usage
9 downloads · 86 views
File structure
📄 SKILL.md
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