effective-html: Agent skills for useful HTML artifacts, wireframes, interactive prototypes, plans, and diagrams

How to install effective-html

npx skills add plannotator/effective-html
npx skills add plannotator/effective-html --list

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 68/100

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.

Quick Facts

About effective-html

HTML skills for pragmatic visual artifacts https://github.com/user-attachments/assets/24306977-7f30-44c9-9bff-55f901d557b0 Note: The diagram was made by , I will create more fable 5 artifacts and add them to the skill folder for smaller models to distill.

Compatible Skills

These tools work well together with effective-html for enhanced workflows:

• agent-skills-playground — semantic(0.18)+complementary+same_lang+similar_pop (51%)
• golang-skills — semantic(0.16)+complementary+same_lang+similar_pop (51%)
• ljg-skill-the-one — semantic(0.23)+complementary+same_lang+similar_pop (48%)

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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:

• SlowMist agent-security taxonomy
• Liu et al., An Empirical Study of Agent Skill Security (2026)
• Our census dataset (DOI 10.5281/zenodo.21292799) · Hugging Face mirror
• How we grade: all 42 rules, the source tiers and known limits
• Full methodology & who runs this