lieflat-charts — security grade SAFE, quality 62/100

Security audit verdict: SAFE · quality 62/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by larashero3-dotcom · Codex Skill · ★ 4.7k

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

🔒 Is lieflat-charts safe to install? View the security audit →

About lieflat-charts

Lieflat Charts Lieflat Charts 是一套遵循 Agent Skills 格式的数据可视化 skill,可供 moxt、Claude Code、Codex 及其他兼容 的 AI agent 使用。本 skill 在 moxt.ai 制作,专注于把数据图表做成有编辑感、能阅读、能组成完整页面的视觉内容。 它以统一的 Mono 灰阶、字体、留白、线条和动效建立自己的视觉语法,包括以下几种视觉风格: Lupi(编辑叙事型):用细线、点阵、逐条记录和大量留白展开数据,强调真实单位、细节和旁注,适合论文、长文、年报与需要慢慢阅读的数据故事。 Glance(快速判断型):用粗柱、大数字、色块和清晰排序提前聚合信息,让读者几秒内看懂高低、变化和异常,适合周报、汇报与 dashboard。 Basics(基础编辑型):保留柱状图、折线图、环形图等熟悉轮廓,再用可数刻度、发丝线和编辑排版增加质感,适合结构简单或数据量较少的内容。 此外还提供网络、路径和多段流向等独立交互大图。每张图都尽量保留数据的真实单位,同时让标题、旁注、来源和页面结构参与表达。 图表的默认色彩是黑白灰单色。当用户明确要求颜色,或颜色承载真实数据维度时,可以从青瓷蓝、椰林绿或编辑部红三套预设起步。预设先保证初稿稳定统一;生成后仍可按需要继续调色,同时保持图型的结构、比例、对比度和数据契约。 Preview 以下是几类模板的实际预览。 Lupi Editorial 细读、逐记录、编辑感。精选 15 张编辑叙事型模板中的代表图型。 Glance 快读、聚合、结论先行。精选 18 张快速判断型模板中的代表图型。 动态预览: <img src="docs/assets/glanc

agent-skillschartsclaude-codecodexdata-visualizationhtmlmoxtsvg

Quick Facts

Stars4,689
Forks276
LanguageHTML
CategoryCodex Skill
Quality Score61.5910833594866/100
Last Updated2026-09-05
Created2026-07-16
Platformsclaude-code, codex
Est. Tokens~17k

Compatible Skills

These tools work well together with lieflat-charts for enhanced workflows:

  • anyviz — semantic(0.55)+complementary+rare_topics+similar_pop+shared_platform (64%)
  • dashmotion — semantic(0.34)+complementary+same_lang+similar_pop+shared_platform (62%)
  • apple-bento-grid — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (60%)
  • ljg-skill-the-one — semantic(0.40)+complementary+same_lang+similar_pop (59%)

lieflat-charts alternative? Top 6 similar tools

Looking for a lieflat-charts alternative? If you're comparing lieflat-charts with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • pilot-shell by maxritter · ⭐ 2.1k

    Professional context and harness engineering for Claude Code and OpenAI Codex. Build production-grade software

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  • superset by superset-sh · ⭐ 14.8k

    Superset is an agentic IDE to orchestrate 100+ coding agents in parallel. Run any agent with your own subscrip

  • fireworks-tech-graph by yizhiyanhua-ai · ⭐ 11.2k

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  • claude-ads by AgriciDaniel · ⭐ 9.7k

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Frequently Asked Questions

What is lieflat-charts?

lieflat-charts is Data visualization Skill for AI Agents, turning data into polished, interactive HTML charts. 面向 AI Agents 的数据可视化 Skill,将数据快速生成精致、可交互的 HTML 图表。. It is categorized as a Codex Skill with 4.7k GitHub stars.

What programming language is lieflat-charts written in?

lieflat-charts is primarily written in HTML. It covers topics such as agent-skills, charts, claude-code.

How do I install or use lieflat-charts?

You can find installation instructions and usage details in the lieflat-charts GitHub repository at github.com/larashero3-dotcom/lieflat-charts. The project has 4.7k stars and 276 forks, indicating an active community.

What are the best alternatives to lieflat-charts?

The top alternatives to lieflat-charts on Agent Skills Hub include pilot-shell, claude-workflow-v2, ai-guide. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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:

View on GitHub → Browse Codex Skill tools