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Skills azure-search-documents-py
📦
azure-search-documents-py
Content revision r2 Safe ⚡ Contains scripts⚙️ External commands🌐 Network access🔑 Env variables
Build Azure AI Search Solutions with Python
Azure AI Search implementations require careful SDK configuration across indexes, vectors, authentication, and query modes. This skill provides focused Python 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-py" from https://skillstore.io/skills/sickn33-azure-search-documents-py.md and its manifest at https://skillstore.io/api/skills/sickn33-azure-search-documents-py/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-py". Design a vector-ready product index with title, description, category, price, and embeddings.
Expected outcome:
- Index plan covering keys, searchable fields, filters, sorting, and vector dimensions.
- HNSW profile and semantic configuration recommendations.
- Validation notes for embedding compatibility and index updates.
Using "azure-search-documents-py". Explain how to combine keyword, vector, semantic, and category filtering in one query.
Expected outcome:
A structured query approach that combines text relevance, nearest-neighbor retrieval, semantic reranking, category filtering, selected fields, and result checks.
Using "azure-search-documents-py". Review a document ingestion flow that uploads large batches with an API key.
Expected outcome:
A review recommending managed identity, minimum roles, buffered batch uploads, retry monitoring, partial-failure handling, and environment-specific load testing.
Security Audit
Safev5 • 7/23/2026 Open versioned report
All static findings are false positives caused by Markdown formatting, static Python imports, Azure placeholders, documented environment access, and search terminology. No command execution, embedded secret, exfiltration, prompt injection, or malicious intent was found.
1
Files scanned
537
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚡ Contains scripts (4)
⚙️ External commands (50)
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🌐 Network access (2)
🔑 Env variables (9)
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 →
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APA citation
sickn33. (2026). azure-search-documents-py security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-azure-search-documents-py/audits/5BibTeX citation
@techreport{sickn33-sickn33-azure-search-documents-py-2026,
author = {sickn33},
title = {azure-search-documents-py security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-azure-search-documents-py/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-py 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-py/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-azure-search-documents-py:audit:5"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
78
Spec Compliance
What You Can Build
Add Search to a Python Service
Create authenticated clients, upload documents, and implement keyword or filtered search for an application.
Design Vector and Hybrid Retrieval
Define vector fields, HNSW profiles, semantic settings, and combined query patterns for relevant retrieval.
Build Enriched Indexing Pipelines
Configure data sources, skillsets, indexers, and batch document operations for managed ingestion.
Try These Prompts
Create a Basic Search Client
Create a basic Azure AI Search Python example using DefaultAzureCredential. Include SearchClient setup, one keyword query, selected fields, and brief setup notes.
Design a Vector Index
Design an index for documents with id, title, content, and a 1536-dimension vector. Add HNSW configuration and explain each field choice.
Implement Hybrid Semantic Search
Draft a hybrid search workflow using keyword text, a vector query, semantic ranking, filters, and selected fields. Include result handling and validation steps.
Review a Production Architecture
Review my Azure AI Search architecture for authentication, indexing, vector dimensions, batching, async usage, and error handling. Identify risks and propose production-ready improvements.
Best Practices
- Prefer DefaultAzureCredential for production and grant the minimum required Azure roles.
- Match vector dimensions and profiles to the embedding model before creating the index.
- Validate batch failures, close clients, and test queries against the target service configuration.
Avoid
- Do not place real API keys in prompts, source files, examples, or logs.
- Do not reuse sample dimensions or field names without checking the embedding model and index schema.
- Do not assume successful batch submission means every document was indexed.
Frequently Asked Questions
Does this skill create Azure resources automatically?
No. It provides Python SDK guidance and examples that require your Azure service, credentials, permissions, and execution environment.
Which authentication method should production applications use?
Prefer DefaultAzureCredential with minimum required Azure roles. Use API keys only when managed identity or Entra ID is unsuitable.
Can it help with vector and hybrid search?
Yes. It covers vector fields, HNSW profiles, vector queries, keyword combinations, semantic ranking, filters, and result selection.
Does it generate embeddings?
No. It expects embeddings from your chosen model or an Azure vectorizer configured for the index.
Can it support asynchronous Python applications?
Yes. It includes an asynchronous SearchClient pattern and recommends context management for client cleanup.
Are the examples ready for production use?
No. Replace placeholders and validate imports, roles, schemas, dimensions, service features, retry behavior, and error handling in 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-pyRef
f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
Maintenance freshness
7/26/2026
Usage
9 downloads · 124 views
File structure
📄 SKILL.md
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