apex: APEX turns Azure platform engineering requirements into verified, deploy-ready IaC

by jonathan-vella · Agent Tool · ★ 220

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

Security audit verdict: SAFE · quality 61/100

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.

agentic-aiagentsanthropicazureazureverifiedmodulesbicepcluadecopilotgithubgpt

Quick Facts

Stars220
Forks80
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score61/100
Open Issues4
Last Updated2026-10-22
Created2025-12-04
Platformspython, vscode
Est. Tokens~5182k

About apex

APEX Agentic Platform Engineering eXperience for Azure. This repository is the source project for a multi-agent workflow that turns Azure platform engineering requirements into deployable Bicep or Terraform with human approval gates across the lifecycle.

Compatible Skills

These tools work well together with apex for enhanced workflows:

  • polyclaw — semantic(0.35)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • remote-mcp-functions-python — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (56%)
  • Agent-Skills — semantic(0.41)+complementary+rare_topics+similar_pop (54%)
  • snippy — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • terrashark — semantic(0.24)+complementary+rare_topics+similar_pop (52%)

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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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