mesurer: UI inspector and visual feedback tool for building with coding agents

by ibelick · Agent Tool · ★ 397

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

How to install mesurer

npm install mesurer

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 67/100

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.

Quick Facts

Stars397
Forks21
LanguageTypeScript
CategoryAgent Tool
Quality Score67/100
Open Issues2
Last Updated2026-08-30
Created2026-04-09
Platformsnode
Est. Tokens~15k

About mesurer

Mesurer Visual precision for building with coding agents. Measure, inspect, and capture exactly what you see in the browser. Mesurer is an open-source UI inspector and visual feedback tool for the browser. Use guides, rulers, measurements, color inspection, typography tools, X-ray mode, screenshots to understand interfaces and communicate precise visual feedback to coding agents.

Compatible Skills

These tools work well together with mesurer for enhanced workflows:

  • makethisbetter-js — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (58%)
  • docfork — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • cui — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)
  • ai-devkit — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (56%)

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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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