gerillass: Sass mixin library containing a set of Sass mixins and functions to help front-end developers generate

by selfishprimate · Agent Tool · ★ 171

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. 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.

aiai-toolsclaude-codecssdeveloper-toolsdevelopment-toolsfront-end-developmentgerillasssasssass-boilerplate

Quick Facts

Stars171
Forks17
LanguageSCSS
CategoryAgent Tool
Quality Score62/100
Last Updated2026-09-16
Created2019-12-23
Platformsbrowser, claude-code
Est. Tokens~18k

About gerillass

Meet the Coolest Sass Toolset! Gerillass is a library built on top of Sass (Syntactically Awesome Style Sheets) to give you flexibility for your projects and accelerate your performance and creativity. It is also built to be read by coding agents.

Compatible Skills

These tools work well together with gerillass for enhanced workflows:

  • Ghost-Agent — semantic(0.20)+complementary+rare_topics+similar_pop+shared_platform (52%)
  • hermes-agent-control-room — semantic(0.18)+complementary+rare_topics+similar_pop+shared_platform (51%)
  • lazy-bird — semantic(0.18)+complementary+rare_topics+similar_pop+shared_platform (51%)

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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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