Azure-Language-OpenAI-Conversational-Agent-Accelerator: A solution accelerator project harnessing the capabilities of Azure AI Language to augment an existing Azure

by Azure-Samples · Agent Tool · ★ 135

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-azd-templatesazd-templatesazure-languageazure-openaiconversational-aipythonreact

Quick Facts

Stars135
Forks116
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score62/100
Open Issues8
Last Updated2025-11-14
Created2024-11-05
Platformspython
Est. Tokens~243k

About Azure-Language-OpenAI-Conversational-Agent-Accelerator

Azure Language OpenAI Conversational Agent Accelerator This accelerator template provides users with a code-first example for creating and enhancing chat solutions and agents with deterministic, human-controllable workflows.

Compatible Skills

These tools work well together with Azure-Language-OpenAI-Conversational-Agent-Accelerator for enhanced workflows:

  • awesome-azure-openai-llm — semantic(0.25)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • litellm — semantic(0.19)+complementary+shared_fw(openai)+rare_topics+same_lang+shared_platform (59%)
  • superagentx — semantic(0.15)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • pal-mcp-server — semantic(0.17)+complementary+shared_fw(openai)+same_lang+shared_platform (49%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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