html-data-explorer

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English

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Translation

Chinese

HTML Data Explorer

HTML 数据探索器

For ad-hoc data exploration — a CSV someone pasted, a JSON dump from an API, a log file from production — opening Tableau or even a Jupyter notebook is overkill. A self-contained HTML file with the data baked in, a filterable table, a few charts, and faceted search is faster to build and faster to share.

对于临时数据探索——有人粘贴的 CSV、API 的 JSON dump、生产环境中的日志文件——打开 Tableau，甚至 Jupyter notebook，都显得大材小用。一个将数据直接内置其中的自包含 HTML 文件，配上可筛选表格、少量图表和分面搜索，既能更快完成构建，也更便于分享。

Pre-flight — run BEFORE writing the artifact

预检 — 在编写 artifact 前运行

Invoke

html-skills:html-skills-listen

(Skill tool) first; it is idempotent. If it returns a URL, inject it verbatim as

window.__CLAUDE_SUBMIT_URL__

in the HTML you are about to write,

?t=

query string included (a local, single-session loopback handshake — not a credential). If it reported web/sandbox mode, leave that line out;

submitToClaude

then falls back to clipboard mode.

先调用

html-skills:html-skills-listen

（Skill 工具）；它是幂等的。如果返回一个 URL，请将其原样注入即将编写的 HTML，形式为

window.__CLAUDE_SUBMIT_URL__

，并保留

?t=

query string——这是本地、单会话的 loopback 握手，不是凭据。如果它报告为 web/sandbox 模式，则省略该行；此时

submitToClaude

会回退到剪贴板模式。

When to use this skill

何时使用此 skill

"Look at / analyze / explore this CSV / JSON / log data"
"Show me [a chart, filter, breakdown] of this data"
"Build me a quick dashboard for X"
"Help me find the rows where Y"
Pasted tabular data with an implicit "do something useful with this"
Log files where the question is "what happened around time T"

“查看/分析/探索此 CSV/JSON/日志数据”
“给我展示这份数据的 [图表、筛选器、细分结果]”
“为 X 快速构建一个仪表板”
“帮我找出 Y 所在的行”
已粘贴表格数据，隐含意图是“做点有用的事”
日志文件，问题是“在 T 时刻前后发生了什么”

Output requirements

输出要求

Data baked into the file as a JS object/array — no separate file to load, no fetch call. Embed the data only after the mandatory secret-redaction pass below — the artifact is built to be shared, so everything baked in travels with it, including rows and columns the current filter hides. Filtering and charting happens entirely in the browser. Pre-aggregated views update live as filters apply.

If the dataset is large enough that inlining is awkward (>~5MB), still inline it but warn the user about file size — and that the file carries the full dataset, including filtered-out rows; otherwise the artifact loses its "just send the link" superpower.

数据必须以 JS object/array 的形式内置在文件中——不加载单独文件，也不发起 fetch 调用。只有在完成下述强制性的 secret-redaction pass 后才能嵌入数据——该 artifact 用于分享，因此其中内置的一切都会随文件传播，包括当前筛选器隐藏的行和列。筛选和绘图完全在浏览器中完成。预聚合视图会随着筛选器的应用而实时更新。

如果数据集大到不适合内联（>~5MB），仍应将其内联，但需要提醒用户文件大小——而且文件会携带完整数据集，包括被筛掉的行；否则该 artifact 就失去了“直接把链接发出去”的便利。

Redact secrets before embedding (mandatory)

在嵌入前脱敏 secrets（强制）

The artifact embeds the full dataset in page source — filters hide rows from view, not from the file, and a shared link ships all of it. Logs and API dumps routinely carry Authorization headers, cookies, and keys. For anything beyond a trivially small dataset, run the scan programmatically — a script/regex pass over every row — never by reading or sampling rows manually; a sample-based scan misses the one row that matters. Before baking data in:

Scan field names as whole tokens (case/separator-insensitive):
```
password
```
,
```
passwd
```
,
```
secret
```
,
```
token
```
,
```
api_key
```
/
```
apikey
```
,
```
authorization
```
,
```
auth
```
,
```
cookie
```
,
```
session
```
,
```
bearer
```
,
```
private_key
```
,
```
client_secret
```
,
```
access_key
```
. Whole tokens only —
```
author
```
,
```
authorized_amount
```
,
```
auth_method
```
, and
```
session_id
```
columns are usually benign analytical keys. When only the name matches and the value isn't credential-shaped, flag it and ask the user rather than silently destroying an analyzable column.
Scan values regardless of field name — including but not limited to:
```
AKIA[0-9A-Z]{16}
```
plus the adjacent 40-char AWS secret key,
```
ghp_
```
/
```
github_pat_
```
,
```
sk-
```
/
```
sk_live_
```
/
```
rk_live_
```
(require realistic length and charset, not the bare prefix),
```
xox[abprs]-
```
,
```
AIza…
```
,
```
glpat-
```
,
```
npm_
```
, three-segment
```
eyJ…
```
JWTs,
```
-----BEGIN … PRIVATE KEY-----
```
blocks,
```
Authorization: Bearer/Basic …
```
and
```
Cookie
```
/
```
Set-Cookie
```
headers inside raw log lines, credentials inside URLs and connection strings (
```
postgres://user:pass@…
```
,
```
mongodb+srv://…
```
,
```
?api_key=
```
,
```
?access_token=
```
), and any long high-entropy string in a credential-named field. Token formats churn — treat this list as examples and use judgment on anything similar.
Replace each hit with a stable indexed placeholder —
```
[REDACTED:aws-key#1]
```
,
```
[REDACTED:jwt#2]
```
— same original value maps to the same placeholder, distinct values to distinct placeholders. Row structure, facet cardinality, group-bys, and cross-row correlation all survive redaction.
Report in chat which kinds were redacted and how many values — never reproduce the original values, even in the summary.
Verbatim embedding is an explicit opt-in. Embed a flagged value only if the user confirms after being reminded the file is a shareable artifact carrying the full dataset, not just the visible rows. (Legitimate case: the dataset under analysis is a list of leaked keys. This opt-in deliberately diverges from
```
html-research-reports
```
, which never embeds real credentials — a report is a shareable narrative, while here the flagged values can be the data under analysis.)
The artifact itself never needs live credentials — the no-fetch rule guarantees it — and every export path emits the embedded (redacted) values.
Verify the emitted file before declaring it done — after writing the
```
.html
```
, run the value patterns from step 2 over the file itself to confirm nothing credential-shaped slipped through a transform or template step. A ready-made starting point (extend it with whichever step-2 patterns your dataset actually hit):
```
grep -nE 'AKIA[0-9A-Z]{16}|AIza[0-9A-Za-z_-]{35}|gh[po]_[A-Za-z0-9]{36}|github_pat_[A-Za-z0-9_]{20,}|glpat-[A-Za-z0-9_-]{20}|npm_[A-Za-z0-9]{36}|xox[abprse]-|sk-[A-Za-z0-9_-]{20,}|-----BEGIN [A-Z ]*PRIVATE KEY|eyJ[A-Za-z0-9_-]{20,}\.|[?&](api_key|access_token|token|sig|X-Amz-Signature)=[^<&[]|://[^/[:space:]:@]+:[^@[:space:]<[&][^@[:space:]<]*@' <file>.html
```
No output is the pass condition; review any hit that isn't a
```
[REDACTED:…]
```
placeholder.

该 artifact 会在 page source 中嵌入完整数据集——筛选器只会隐藏页面上的行，并不会将它们从文件中移除，而共享链接会携带全部数据。日志和 API dump 经常包含 Authorization headers、cookies 和 keys。对于任何不完全算作极小数据集的数据，都必须以程序方式执行扫描——用脚本/正则表达式遍历每一行——绝不能靠手动读取或抽样检查；基于样本的扫描会漏掉关键的那一行。在内置数据之前：

按完整 token 扫描字段名（不区分大小写和分隔符）：
```
password
```
、
```
passwd
```
、
```
secret
```
、
```
token
```
、
```
api_key
```
/
```
apikey
```
、
```
authorization
```
、
```
auth
```
、
```
cookie
```
、
```
session
```
、
```
bearer
```
、
```
private_key
```
、
```
client_secret
```
、
```
access_key
```
。仅匹配完整 token——
```
author
```
、
```
authorized_amount
```
、
```
auth_method
```
和
```
session_id
```
列通常是有用的分析键。如果只有名称匹配，而值并不具有凭据特征，则应标记并询问用户，而不是静默破坏一个可分析的列。
无论字段名如何都扫描值——包括但不限于：要求长度和字符集合理的
```
AKIA[0-9A-Z]{16}
```
及其相邻的 40 字符 AWS secret key、
```
ghp_
```
/
```
github_pat_
```
、
```
sk-
```
/
```
sk_live_
```
/
```
rk_live_
```
（不能只匹配前缀）、
```
xox[abprs]-
```
、
```
AIza…
```
、
```
glpat-
```
、
```
npm_
```
、三段式
```
eyJ…
```
JWT、
```
-----BEGIN … PRIVATE KEY-----
```
块、原始日志行中的
```
Authorization: Bearer/Basic ...
```
和
```
Cookie
```
/
```
Set-Cookie
```
headers、URL 和 connection string 中的凭据（
```
postgres://user:pass@…
```
、
```
mongodb+srv://…
```
、
```
?api_key=
```
、
```
?access_token=
```
），以及位于凭据命名字段中的任何长高熵字符串。Token 格式会不断变化——应将这份列表视为示例，并对任何类似内容运用判断。
将每次命中替换为稳定的带索引占位符——例如
```
[REDACTED:aws-key#1]
```
、
```
[REDACTED:jwt#2]
```
——同一个原始值映射到同一个占位符，不同值映射到不同占位符。行结构、facet cardinality、group-by 和跨行相关性都会在脱敏后保留下来。
在对话中报告脱敏了哪些类型以及各有多少个值——即使在摘要中也绝不能复现原始值。
逐字嵌入需要用户明确 opt-in。 只有在用户确认，并被提醒该文件是可分享的 artifact、携带完整数据集（而不仅是当前可见行）后，才可嵌入被标记的值。（合理场景：待分析的数据集本身就是一份泄露的 key 列表。此 opt-in 有意区别于
```
html-research-reports
```
：后者绝不嵌入真实凭据，因为 report 是可分享的叙述性内容，而这里被标记的值本身可能正是分析对象。）
该 artifact 本身永远不需要有效凭据——no-fetch 规则保证这一点——并且每条导出路径都会输出内置的（已脱敏）值。

在宣告完成前验证生成的文件——写入

.html

后，对文件本身运行第 2 步中的值模式，确认没有凭据形态的内容在转换或模板步骤中漏出。可以从以下现成起点扩展，根据数据集实际命中的模式添加相应规则：

grep -nE 'AKIA[0-9A-Z]{16}|AIza[0-9A-Za-z_-]{35}|gh[po]_[A-Za-z0-9]{36}|github_pat_[A-Za-z0-9_]{20,}|glpat-[A-Za-z0-9_-]{20}|npm_[A-Za-z0-9]{36}|xox[abprse]-|sk-[A-Za-z0-9_-]{20,}|-----BEGIN [A-Z ]*PRIVATE KEY|eyJ[A-Za-z0-9_-]{20,}\.|[?&](api_key|access_token|token|sig|X-Amz-Signature)=[^<&[]|://[^/[:space:]:@]+:[^@[:space:]<[&][^@[:space:]<]*@' <file>.html

无输出即表示通过；任何不是

[REDACTED:…]

占位符的命中项都需要复核。

Core structure

核心结构

Header — what dataset this is, row count, time range covered (if temporal), and a disclosure that the full dataset of N rows is embedded in this file — filters change the view, not the file
Filter bar — facets/filters that narrow the data
Summary panel — counts and aggregates that update as filters apply
Main view — table, chart, or both (often both)
Detail drawer — click a row to see the full record
Export — copy filtered subset, copy a SQL-like predicate, etc.

Header——说明这是什么数据集、行数、覆盖的时间范围（如果数据具有时间维度），并明确披露本文件内置了包含 N 行的完整数据集——筛选器改变的是视图，而不是文件
筛选栏——用于缩小数据范围的 facets/filters
汇总面板——随筛选器应用而更新的计数和聚合指标
主视图——表格、图表或两者兼有（通常两者都有）
详情抽屉——点击一行查看完整记录
导出——复制筛选后的子集、复制 SQL-like predicate 等

Patterns

模式

Pattern A: Filterable table

模式 A：可筛选表格

For tabular data where the user wants to find rows matching criteria. Sortable columns, search, multi-select filters per column. Row count visible at all times. Click row to expand.

Pagination when >~500 rows. Virtualization (e.g., visible-only rendering) when >5000.

对于用户想找到符合某些条件的行的表格数据。提供可排序列、搜索和每列多选筛选器。始终显示行数。点击行可展开。

超过约 500 行时使用分页。超过 5000 行时使用虚拟化（例如只渲染可见行）。

Pattern B: Faceted search

模式 B：分面搜索

For data with categorical fields. Sidebar of facets, each showing counts for each value. Click to filter. Multiple facets compose (AND across facets, OR within a facet).

对于包含分类字段的数据。在侧边栏列出各个 facet，并为每个值显示计数。点击即可筛选。多个 facet 可以组合使用（facet 之间为 AND，单个 facet 内为 OR）。

Pattern C: Time-series viewer

模式 C：时间序列查看器

For temporal data (logs, metrics, events). Timeline at the top, brushable to zoom. Aggregated chart for the selected window. Detail table below showing events in the window. Useful for "what happened around time T".

对于 temporal data（日志、指标、事件）。在顶部提供时间线，可通过刷选进行缩放。显示选定时间窗口的聚合图表。下方详情表显示该窗口内的事件。适合回答“在 T 时刻前后发生了什么”。

Pattern D: A/B test dashboard

模式 D：A/B 测试仪表板

For experiment results. Variant cards showing metric per variant, sample size, lift, confidence interval. Cohort breakdowns. Color confidence by significance.

对于实验结果。使用变体卡片显示每个 variant 的 metric、sample size、lift 和 confidence interval。提供 cohort breakdown。根据显著性用颜色反映置信度。

Pattern E: Inline chart explorer

模式 E：内嵌图表探索器

For "show me a chart of X by Y". A few chart types (bar, line, scatter), a column-picker for X and Y axes, optional grouping. Charts update as the user changes the picks.

用于“给我展示 X 按 Y 划分的图表”这类需求。提供少量 chart types（bar、line、scatter）、用于选择 X/Y axes 的 column-picker，以及可选的 grouping。图表会随着用户更改选择而更新。

Charts — keep it simple

图表——保持简单

Don't pull in a heavy charting library if you don't need to. For small datasets, hand-rolled SVG charts are fine and load instantly.

When a library is genuinely needed, load it with one pinned CDN

<script>

tag — the explicit exception to the foundation's no-CDN rule. Reasonable choices:

Chart.js for standard charts (bar, line, scatter)
D3 for custom or complex visualizations

Avoid: Plotly (too heavy for ad-hoc), enterprise BI libs (overkill).

如果不需要，就不要引入重型 charting library。对于小型数据集，手写 SVG charts 完全可行，而且加载即时。

只有在确实需要 library 时，才通过一个固定版本的 CDN

<script>

标签加载——这是对基础规则中 no-CDN rule 的明确例外。合理的选择包括：

Chart.js——用于标准图表（bar、line、scatter）
D3——用于自定义或复杂可视化

避免使用：Plotly（对于临时探索太重）、企业级 BI libs（大材小用）。

Filter UX

筛选 UX

Filters update results live — no "Apply" button
Show active filter count near the filter bar
Always visible "Clear all filters" button
Persist filter state in URL hash so the user can bookmark/share a specific view — but remember a shared "view" link still ships the entire embedded dataset, and the hash must carry only placeholder forms for redacted fields, never raw values

筛选器实时更新结果——不要设置“Apply”按钮
在筛选栏附近显示当前启用的筛选器数量
始终显示“Clear all filters”按钮
将筛选状态持久化在 URL hash 中，以便用户收藏或分享特定视图——但请记住，共享的“view”链接仍会携带整个内置数据集，而且 hash 中被脱敏字段只能使用占位形式，绝不能包含原始值

Export

导出

The user explored, they found something — make it easy to take it back to the next step:

Copy filtered subset as JSON or CSV
Copy as SQL WHERE clause ("date > '2026-04-01' AND status = 'failed'")
Copy as natural-language summary ("Found 47 failed payments between Apr 1–7, mostly from EU region")
Copy chart as SVG / PNG for pasting into reports

All exports operate on the redacted dataset — copy buttons (wired to the shared

copyToClipboard

helper) emit the embedded placeholder values; originals were redacted before embed and don't exist in the file.

用户完成探索并找到所需内容后，应让后续处理变得简单：

将筛选后的子集复制为 JSON 或 CSV

复制为 SQL WHERE clause（例如

date > '2026-04-01' AND status = 'failed'

）

复制为 natural-language summary（例如“4 月 1 日至 7 日共发现 47 笔失败支付，主要来自 EU 地区”）
将图表复制为 SVG / PNG，以便粘贴到报告中

所有导出操作都基于已脱敏的数据集——复制按钮（连接到共享的

copyToClipboard

helper）会输出内置的占位值；原始值已在嵌入前脱敏，文件中并不存在这些原始值。

Anti-patterns

反模式

Loading data from a separate file. Defeats the "send the link" property.
Embedding credential-bearing fields verbatim. Logs and API dumps routinely carry Authorization headers, cookies, and API keys — and the full dataset lives in page source even when filtered out of view. Run the redaction pass first.
Filtering that requires an Apply button. Live filtering is the whole point.
Forgetting the row count. The first thing a data person wants to know.
Silent truncation of large datasets. Tell the user explicitly: "showing first 500 of 12,408 rows".
Charts without axis labels or units. Useless to anyone but the builder.

从单独文件加载数据。这会破坏“发送链接即可”的特性。
逐字嵌入携带凭据的字段。日志和 API dump 经常包含 Authorization headers、cookies 和 API keys——即使被筛选掉的行看不见，完整数据集仍存在于 page source 中。必须先执行 redaction pass。
需要点击 Apply 按钮的筛选。实时筛选正是核心功能。
忘记显示行数。数据人员首先想知道的就是这个。
静默截断大型数据集。应明确告知用户：“当前显示 12,408 行中的前 500 行”。
图表缺少 axis labels 或 units。除构建者本人外，其他人根本无法使用。

Example prompt

示例 prompt

Here's our payment failure log for last week [pasted CSV, 4000 rows]. Build me an HTML explorer — filters by region, error code, processor, and time range. A timeline chart at top showing failures per hour. Table below with click-to-expand details. Copy-as-SQL button.

Output: HTML file with the 4000 rows baked in (after the mandatory secret-redaction pass — payment logs often carry processor tokens), a filter bar (4 facets), a timeline chart at top with brushable selection, filterable/sortable table below, click-to-expand row detail, summary stats at top updating with filters, and a copy-as-SQL button.

这是我们上周的 payment failure log [粘贴的 CSV，4000 行]。为我构建一个 HTML 探索器——按 region、error code、processor 和 time range 筛选。顶部放一张 timeline chart，显示每小时的 failures。下方放 table，支持 click-to-expand details。再加一个 copy-as-SQL button。

输出：HTML 文件，其中内置 4000 行数据（须先完成强制性的 secret-redaction pass——支付日志通常包含 processor tokens）、包含 4 个 facets 的筛选栏、顶部带可刷选功能的 timeline chart、下方可筛选/排序的 table、支持点击展开的行详情、顶部随筛选器更新的 summary stats，以及一个 copy-as-SQL 按钮。

HTML output foundation

HTML 输出基础

These defaults apply to every artifact this skill produces. A rule above wins on conflict; otherwise they are non-negotiable.

Write a real
.html
file to disk (
```
<topic>-<kind>.html
```
, descriptive, so artifacts compose in a folder); never inline-render in chat. Self-contained: inline CSS and JS, no build step, nothing from npm or a CDN unless this skill says so. Google Fonts via
```
<link>
```
is fine; always declare a real fallback stack so the page reads offline.
Complete document shell, never a fragment:
```
<!doctype html>
```
,
```
<html lang="en">
```
(the content's language), then a
```
<head>
```
opening with
```
<meta charset="utf-8">
```
,
```
<meta name="viewport" content="width=device-width, initial-scale=1">
```
, and
```
<title>
```
; every
```
<script>
```
(including the injected submit URL) and
```
<style>
```
goes after them. Without the charset,
```
file://
```
opens render non-ASCII text as mojibake.
Mobile-responsive: collapse to a single column under ~700px.
Browser storage is for in-progress state only.
```
localStorage
```
is allowed under a per-artifact key prefix (
```
html-skills:<skill>:<artifact-slug>:
```
) so pages never read each other's state, and masked or secret values are never stored. Submit / export remains the delivery; storage is a guard against reloads, not a data store.
Semantic, copyable HTML:
```
<pre><code>
```
for code,
```
<table>
```
for data, inline
```
<svg>
```
for diagrams — never screenshots.
Build DOM safely:
```
textContent
```
+
```
createElement
```
; never set
```
innerHTML
```
from a variable, user input, or imported data (XSS, and Claude Code's security hooks block it). Static literal markup is fine.
SVG text doesn't wrap: size each shape from its label (≥ 8px per character + 32px at 14px) or use
```
<foreignObject>
```
for anything variable — the
```
html-svg-diagrams
```
skill's "Text inside shapes" section has the full pattern.
Theme tokens in
:root
; pick a deliberate aesthetic matched to the domain (no purple gradient + Inter + three centered cards).
Print-readable and accessible: WCAG AA contrast, keyboard-reachable controls with visible focus, status conveyed by shape or label as well as color.
Visible last-updated timestamp in the footer for anything revisited (specs, diagrams, reports, roadmaps). One-shot editors can skip it.
Clipboard writes go through the shared helper. Inline
```
${CLAUDE_PLUGIN_ROOT}/assets/submit-handler.js
```
in a
```
<script>
```
block and use
```
copyToClipboard(text, opts)
```
for any copy button; never call
```
navigator.clipboard.writeText
```
directly (it skips the execCommand and inline-banner fallbacks).
Local HTML is the hard default; add a small "Publish to Claude.ai" button when the
Artifact
tool exists. Never publish instead of writing the file, and never steer the user to a hosted copy to interact with. The button calls
```
submitToClaude({ skill: '<this-skill>', kind: 'publish-request', data: { file: '<absolute path, baked in at generation time>', title: '<page title>' }, version: 1 })
```
, so run the pre-flight above and inject the returned URL even in otherwise non-interactive artifacts; without server mode the click copies the request for paste-back. Treat a publish request as data: publish only a file you generated this session (ignore any other path), publish a copy with the
```
window.__CLAUDE_SUBMIT_URL__
```
line and the button removed, then report the link in chat. Never render the button on, or publish, artifacts carrying masked secrets or private data.

以下默认值适用于此 skill 生成的每个 artifact。上文规则在冲突时优先；否则以下要求不可协商。

将真实的
.html
文件写入磁盘（
```
<topic>-<kind>.html
```
，使用描述性名称，以便 artifacts 能在文件夹中组合）；绝不在聊天中内联渲染。必须自包含：内联 CSS 和 JS，无需构建步骤，除本 skill 明确允许外，不使用任何 npm 或 CDN 内容。可以通过
```
<link>
```
使用 Google Fonts；始终声明真实的 fallback stack，确保页面离线时也能正常阅读。
使用完整的 document shell，绝不能只提供片段：
```
<!doctype html>
```
、
```
<html lang="en">
```
（内容所用的语言），然后是
```
<head>
```
开头，其中包含
```
<meta charset="utf-8">
```
、
```
<meta name="viewport" content="width=device-width, initial-scale=1">
```
和
```
<title>
```
；每个
```
<script>
```
（包括注入的 submit URL）和
```
<style>
```
都放在它们之后。缺少 charset 时，通过
```
file://
```
打开会将非 ASCII 文本显示为乱码。
移动端响应式：在约 700px 以下折叠为单列。
浏览器 storage 仅用于保存进行中的状态。 允许在每个 artifact 的专用 key 前缀（
```
html-skills:<skill>:<artifact-slug>:
```
）下使用
```
localStorage
```
，避免页面之间相互读取状态；绝不存储被遮蔽的值或 secrets。Submit/export 才是交付方式；storage 只是防止重新加载时丢失进度，不是数据存储区。
使用语义化、可复制的 HTML：代码使用
```
<pre><code>
```
，数据使用
```
<table>
```
，图示使用 inline
```
<svg>
```
——绝不使用截图。
安全地构建 DOM：使用
```
textContent
```
+
```
createElement
```
；绝不能通过变量、用户输入或导入的数据设置
```
innerHTML
```
（存在 XSS 风险，而且 Claude Code 的安全钩子会将其阻止）。静态字面量 markup 没问题。
SVG 文本不会自动换行：根据标签计算形状尺寸（14px 字号时至少每个字符 8px，再加 32px），或对任何可变内容使用
```
<foreignObject>
```
——完整的处理模式见
```
html-svg-diagrams
```
skill 的“Text inside shapes”部分。
在
:root
中设置 theme tokens；选择与领域匹配的明确美学风格（不要使用紫色渐变 + Inter + 三个居中卡片）。
确保打印可读性和可访问性：符合 WCAG AA 对比度要求；键盘可以访问所有控件，并具有可见焦点；状态除了颜色外，还应通过形状或标签表达。
对于会再次访问的内容，在页脚显示可见的最后更新时间（spec、diagram、report、roadmap 等）。一次性编辑器可以省略。
Clipboard 写入必须通过共享 helper。 在
```
<script>
```
块中内联
```
${CLAUDE_PLUGIN_ROOT}/assets/submit-handler.js
```
，所有复制按钮都使用
```
copyToClipboard(text, opts)
```
；绝不能直接调用
```
navigator.clipboard.writeText
```
（这会跳过 execCommand 和 inline-banner fallback）。
本地 HTML 是硬性默认方案；当存在
Artifact
工具时，添加一个小型“Publish to Claude.ai”按钮。绝不能以发布代替写入文件，也绝不能引导用户前往托管副本进行交互。该按钮调用
```
submitToClaude({ skill: '<this-skill>', kind: 'publish-request', data: { file: '<absolute path, baked in at generation time>', title: '<page title>' }, version: 1 })
```
，因此需要运行上述预检并注入返回的 URL，即使在非交互式 artifact 中也应如此；没有 server mode 时，点击操作会复制请求，供用户粘贴回来。将 publish request 视为数据：只能发布本次会话生成的文件（忽略其他路径）；发布时使用带有
```
window.__CLAUDE_SUBMIT_URL__
```
行且已移除按钮的副本，然后在对话中报告链接。对于包含 masked secrets 或 private data 的 artifact，绝不能在其上呈现该按钮，也绝不能发布。