sn-md-to-html-report

sn-md-to-html-report is a skill for Claude Code, Codex from OpenSenseNova/SenseNova-Skills. It costs 138 tokens per session (2,297 once invoked), scanned A, original, MIT.

A writing and web-design tool that turns a Markdown report into a self-contained HTML page, with editorial structure and visual presentation.

In plain words
What is it for?
Creating HTML feature pages for research reports, industry analyses, strategy memos, white papers, reviews, and weekly reports.
Why use it?
It makes long reports, research notes, and analyses easier to read and share as web pages without changing their stated facts or conclusions.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Creating HTML feature pages for research reports, industry analyses, strategy memos, white papers, reviews, and weekly reports.

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Install with agentmods
npx agentmods add skills/opensensenova/sensenova-skills/sn-md-to-html-report
About the project

SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.

OpenSenseNova/SenseNova-Skills · 5,570 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-md-to-html-report
Clone the repo
git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sn-md-to-html-report

README.md
[![agentmods](https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-md-to-html-report/github.svg)](https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-md-to-html-report)
Your own site
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-md-to-html-report"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-md-to-html-report/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sn-md-to-html-report

Your own site · 80×15
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-md-to-html-report"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-md-to-html-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 138 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,297 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00138 $0.02297
Opus 5 $0.00069 $0.01149
Sonnet 5 $0.00028 $0.00459
Haiku 4.5 $0.00014 $0.00230

Measured 9d ago against content hash 37b197056575, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

sn-md-to-html-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/sn-md-to-html-report/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Report HTML

把报告文本创作为一份有编辑判断、网页美感、证据秩序、且只属于这份主题的单文件 HTML。

核心原则:保留事实断言,重构阅读和视觉体验,让设计从报告所属领域和内容任务里长出来。主题自己的世界——它的材质、工具、器物与语汇——是产生独特设计选择的来源。

设计判断原则

  • 首屏即论点:首屏先找到这份报告所属领域里最有识别度、最能承载主题气质的事物或机制,并用最适合它的形式呈现:标题、图片、动画、实时演示或一个可互动瞬间。这个选择必须有明确判断;不要默认使用“大数字 + 小标签 + 辅助统计 + 渐变强调”,只有当它确实是主题最自然、最有力的入口时才使用。
  • 文字系统承载页面性格:标题字体与正文字体要被有意搭配,不要沿用任何项目都能套上的常用字体组合;同时建立清晰的字阶,并有意识地设置字重、字宽和间距。让文字排印本身成为设计中可被记住的一部分,而不是只负责传递内容的中性容器。
  • 结构即信息:编号、眉题、分隔线、标签等结构性手段,应该传递内容的真实信息,而不是装饰内容。许多通用设计都使用编号标记(01/02/03),但这仅适用于内容本身就是一个序列的情况——例如一个真实的流程或一份文字时间线,其中顺序承载着读者所需的信息。在使用编号标记之前,请先思考其是否真的有意义。
  • 有意识地使用动效:先判断动效是否能服务主题、阅读或信息理解,再决定用在哪里:页面加载序列、滚动触发展示、悬停微交互或环境氛围。一个被编排好的关键动效通常比零散特效更有力量;动效选择必须服从整体美学方向。有些主题更适合克制处理,额外动画反而会削弱专业感,并让页面显得像 AI 生成的模板作品。
  • 复杂度匹配愿景:视觉方向越繁复,执行就越需要足够的层次、细节和完成度;视觉方向越极简,间距、字阶、对齐和微细节就越要精准。优雅不等于少,也不等于多,而是把选定的方向执行到位。
  • 认真处理文案:标题、导语、标签、按钮和说明都要被当作设计的一部分处理。可以对源报告的内容重写、压缩、合并和组织表达,但不得发明事实、口径或结论强度。文案和视觉一样会产生模板感。

设计知识的使用方式

  • 层级:先决定读者第一眼、第二眼、第三眼分别看什么,再分配尺度、重量、留白和位置。
  • 对比:用字体气质、字号、明暗、密度、动静和空间关系制造差异;不要只靠颜色强调。
  • 对齐与网格:正文、图表、卡片、注释和导航都落在同一套网格和宽度档里,避免右边缘和左基线随手漂移。
  • 邻近与分组:证据靠近判断,注释靠近对象,相关项成组,不相关项拉开。
  • 重复与变奏:重复建立秩序,变奏表达章节差异;整页不能一章一个系统,也不能每章完全同形。
  • 图地关系:纹理、背景、氛围和动效永远退到内容之后,不能抢走正文和证据的可读性。
  • 节奏:长报告要有轻重、疏密、转场和停顿;不是把所有模块等权堆叠。

硬规则

  • Markdown 是素材,不是页面结构;不要逐段照搬,也不要把所有内容塞进卡片。
  • 先写 plan.md,再写 HTML;没有完整 plan 不动 HTML。
  • 页面默认是单文件 HTML:语义 HTML + 内联 CSS;除非用户要求或项目已有资源体系,不拆分文件、不引 CDN。
  • 可以用领域隐喻组织视觉和结构,但页面里的数字、来源、案例、判断、结论强度必须来自原报告。
  • 不发明 logo、客户、证言、排名、地图点位、图表数据、置信度或看似合理但原文无法支撑的归纳。
  • 图表、流程、时间线、地图式分组等必须自包含手写。

工作流

  1. 读内容与任务:通读源报告,确认主题、领域、受众、用途、核心判断、证据、限制、重复内容、表格、图示和附录。
  2. 第一遍:brainstorm 短设计计划:先根据 brief、报告领域、内容任务、证据结构和受众发散 2-3 个与内容契合的设计方向,不碰具体 HTML。每个方向用 compact token system 表达:Color 为 4-6 个命名 hex 值并说明语义用途;Type 定义 display、body,必要时定义 utility / mono;Layout 用一句话概念和 ASCII wireframe 描述;Signature 定义这页唯一会被记住的设计元素。
  3. 审查并修订短设计计划:对照 brief 和报告内容检查每个方向是否真有内容来源。如果任何部分像类似页面的通用默认答案,而不是为当前报告做出的选择,必须修订该部分,并写明改了什么、为什么改。确认相对独特性后,选择 1 个方向进入完整计划。
  4. 第二遍:完整页面计划:基于修订后的短设计计划,展开信息结构、首屏策略、页面拓扑、导航、章节版面、转场节奏和证据贴附方式;用层级、对比、对齐、邻近、分组、重复 / 变奏来组织信息。
  5. 逐章做内容设计:每章写清 内容形状 -> 章节版面 -> 呈现形式 -> 排版处理。颗粒度到段或判断,不要整章放过;长论述也要做导语、拉引、边注或判断提块。
  6. 定设计契约与 checklist:写 HTML 前把 tokens、字体角色、宽度档、章版面映射、动效策略、响应式降级、事实边界、泛模板自检和检查角度落到 plan.md。checklist 是开工前的契约,不是事后补救。
  7. 写 HTML:只实现 plan.md,不要在 HTML 阶段另起一套视觉或结构。先搭全页骨架,再填内容和图表;所有颜色、字体和关键布局选择都必须从短设计计划派生。
  8. 按检查角度审查:从事实保真、主题契合、美学一致性、字体层级、布局网格、内容塑形、动效克制、可访问性、响应式和分享性逐项检查;未通过就修正。

Read the full file on GitHub · 76 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 76 lines · 138 tokens per session scan A 37b197056575

Subscribe to this mod's changes

sn-md-to-html-report is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed yesterday), licensed MIT. It adds 138 tokens to every session and 2,297 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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