envkit-releases: EnvKit — free local development environment for Windows & macOS

by Env-Kit · MCP Server · ★ 618

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Security audit verdict: SAFE · quality 70/100

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. Full audit →

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.

apachedevelopment-environmentherd-alternativelaragon-alternativelaravellocal-developmentmailpitmariadbmcpmongodb

Quick Facts

Stars618
Forks22
LanguagePython
CategoryMCP Server
Quality Score70/100
Open Issues12
Last Updated2026-11-06
Created2026-06-20
Platformsbrowser, mcp, python
Est. Tokens~16k

About envkit-releases

EnvKit Your local stack, one place. A free local development environment for Windows and macOS: nginx or Apache, any PHP version, MySQL, PostgreSQL, Redis and trusted HTTPS, from one desktop app. A Laragon, XAMPP and Herd alternative. Download for Windows · Download for macOS · Website · Docs · Changelog <img src="art/envkit-demo.webp" alt="EnvKit in action: first-run site,

Compatible Skills

These tools work well together with envkit-releases for enhanced workflows:

  • FlyEnv — semantic(0.67)+rare_topics+similar_pop+shared_platform (63%)
  • ServBay — semantic(0.34)+complementary+rare_topics+similar_pop+shared_platform (61%)
  • cognidb — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)
  • gobii-platform — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (55%)

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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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