azure-openai-llm-notes: A curated collection of resources for Azure OpenAI, LLMs (+RAG, Agents). Monthly Updates

by kimtth · Agent Tool · ★ 422

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

Security audit verdict: SAFE · quality 52/100

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.

agentagent-frameworkagent-skillsagentic-aiai-agentsai-engineeringai-toolsawesomeazurecoding-agent

Quick Facts

Stars422
Forks64
LanguagePython
CategoryAgent Tool
Quality Score52/100
Open Issues1
Last Updated2026-11-05
Created2023-04-13
Platformspython
Est. Tokens~18k

About azure-openai-llm-notes

Azure OpenAI + LLM A comprehensive, curated collection of resources for Azure OpenAI, Large Language Models (LLMs), and their applications. 🔹Concise Summaries: Each resource is briefly described for quick understanding 🔹Chronological Organization: Resources appended with date (first commit, publication, or paper release) 🔹Monthly Updates: The list is updated monthly; candidate entries before...

Compatible Skills

These tools work well together with azure-openai-llm-notes for enhanced workflows:

  • superagentx — semantic(0.22)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (70%)
  • Hands-On-AI-Engineering — semantic(0.18)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • agent-architecture-review-sample — semantic(0.18)+complementary+shared_fw(openai)+rare_topics+same_lang+similar_pop+shared_platform (69%)
  • Agent-Skills — semantic(0.41)+complementary+shared_fw(openai)+rare_topics+similar_pop+shared_platform (67%)

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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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