# HTML Data Explorer

> Build HTML data explorers for CSV, JSON, log, and event data — filterable tables, faceted search, inline charts, timeline scrubbing, A/B test result dashboards. Use whenever the user has a dataset to explore, browse, filter, chart, facet, or analyze — especially for ad-hoc analysis where opening a heavy BI tool is overkill. Reach for this when the user pastes data, mentions a CSV/JSON/log file, or asks to "look at" or "analyze" a dataset. Always runs a secret-redaction pass before embedding data — credential-shaped values (API keys, tokens, cookies, passwords) are replaced with placeholders unless the user explicitly opts in to embedding them.

- Skill: `f-labs-io/html-data-explorer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add f-labs-io/html-data-explorer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/f-labs-io/html-data-explorer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- License: MIT
- Author: f-labs-io (https://skillmd.com/u/f-labs-io)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/f-labs-io/html-data-explorer

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# HTML Data Explorer

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.

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## Pre-flight — run BEFORE writing the artifact

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.
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## When to use this 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"

## Output requirements

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.

## Redact secrets before embedding (mandatory)

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:

1. **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.
2. **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.
3. **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.
4. **Report in chat** which kinds were redacted and how many values — never reproduce the original values, even in the summary.
5. **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.)
6. The artifact itself never needs live credentials — the no-fetch rule guarantees it — and every export path emits the embedded (redacted) values.
7. **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.

## Core structure

1. **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
2. **Filter bar** — facets/filters that narrow the data
3. **Summary panel** — counts and aggregates that update as filters apply
4. **Main view** — table, chart, or both (often both)
5. **Detail drawer** — click a row to see the full record
6. **Export** — copy filtered subset, copy a SQL-like predicate, etc.

## Patterns

### Pattern A: Filterable table

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.

### Pattern B: Faceted search

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).

### Pattern C: Time-series viewer

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".

### Pattern D: A/B test dashboard

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

### Pattern E: Inline chart explorer

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.

## Charts — keep it simple

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).

## Filter 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

## Export

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.

## Anti-patterns

- 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.

## Example 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.

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## HTML output foundation

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.
- **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.
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