Skip to main content
skill CX Codex

pdf-to-html — Claude Code skill: turn PDFs into readable HTML

Turns a PDF into one self-contained HTML file that keeps images, tables and reading order. Handy when you want to read a report on your phone instead of scrolling pages.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add daymade/claude-code-skills --skill pdf-to-html -a claude-code
Open on GitHub

What pdf-to-html does

The skill runs a pipeline: extract, look, optionally translate, build, verify. First extract_pdf.py pulls text blocks with their font sizes plus image blocks, flags decorative ones, and renders one PNG per page. The agent then reads those PNGs. Skip that step and you are laying out blind, which is how charts and tables go missing.

Building is handled by build_html.py. It infers heading levels from font size, drops decorative images and inlines content images as base64, so the result is a single portable file that opens on a double-click. Translation is optional: a parallel workflow translates pages and chart captions, reconciles terminology, and writes two overlay files, units.json and caps.json.

You need uv and Google Chrome or Chromium before starting. PyMuPDF, Pillow and numpy come through uv run --with, so there is nothing to pre-install. Scanned PDFs with no text layer need OCR first. Complex multi-column tables lose their grid, because PyMuPDF reads a table as text blocks — the cell text survives, the column alignment does not. For those pages doc-to-markdown is the better pick.

What it helps with

  • read a PDF report as a web page on a phone
  • produce one HTML file with images and tables inlined
  • translate a PDF while keeping charts and captions
  • render PDF pages to PNG and eyeball the layout
  • check the built HTML in headless Chrome for clipped tables

Example prompts for the agent

  • turn this PDF into a readable HTML page
  • translate this report into English but keep the charts
  • I want to read this PDF on my phone — build the HTML

How to install pdf-to-html

1Skills CLI (Vercel)

npx skills add daymade/claude-code-skills --skill pdf-to-html -a claude-code

2Manually: put the files in place

~/.claude/skills/pdf-to-html/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/pdf-to-html/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about pdf-to-html

How do I install pdf-to-html in Claude?

Run npx skills add daymade/claude-code-skills --skill pdf-to-html -a claude-code. The skill lands in ~/.claude/skills/pdf-to-html/SKILL.md.

What does pdf-to-html do?

It converts a PDF into one self-contained HTML file, preserving images, tables, charts and reading order. It can also translate the document into another language while keeping every figure in place.

What does pdf-to-html need to run?

uv to run Python with inline dependencies, and Google Chrome or Chromium for visual verification. PyMuPDF, Pillow and numpy are pulled in through uv run --with.

Source text

SKILL.md · MIT
---
name: pdf-to-html
disable-model-invocation: true
description: Converts a PDF into one self-contained, readable HTML file that preserves images, tables, charts and reading order — optionally translating it into another language while keeping every figure. Uses structured extraction (PyMuPDF), font-size-driven layout, compressed base64-inlined images (a single portable file), and mandatory headless-Chrome visual verification. Use whenever someone wants to READ a PDF as a web page or clean document, turn a PDF into HTML, or translate a PDF into another language while keeping its images/tables/charts intact — e.g. "PDF 转 HTML", "把这个 PDF 转成中文网页版", "make this report readable", "translate this PDF but don't lose the charts", "I just want to read this PDF on my phone". Distinct from doc-to-markdown (plain Markdown text) and pdf-creator (Markdown→PDF) — this one produces a styled, image-faithful HTML reading experience.
---

# PDF to HTML

Turn a PDF into a single, self-contained, readable HTML file — images, tables,
charts and reading order preserved — and optionally translate it, keeping every
figure in place.

The pipeline is **extract → look → (translate) → build → verify**. The middle
"look" and final "verify" steps are where faithfulness actually comes from: a PDF
is a layout, not just a text stream, so you read the rendered pages before
building and the rendered HTML before delivering.

This skill runs **inline** (no `context: fork`): translation orchestrates a
Dynamic Workflow, and a subagent cannot spawn one.

## When to use / not use

- **Use** when the goal is to *read* a PDF as HTML/web page, to convert a PDF to
  a styled HTML document, or to translate a PDF into another language while
  keeping its figures and tables.
- **doc-to-markdown** instead if they want plain Markdown text (no styling, figures optional).
- **pdf-creator** instead for the reverse direction (Markdown → PDF).

## What it does NOT do

- **Scanned/image-only PDFs** (no text layer): OCR first (e.g. `ocrmypdf`), then use this.
- **Complex multi-column tables**: cell *text* is preserved and readable, but column
  alignment can flatten into a text flow — PyMuPDF reads a table as text blocks, not a
  grid, so the grid lines are gone. Tables that are *images* in the PDF survive as
  images. If the table's grid structure is essential, use **doc-to-markdown** (pandoc
  rebuilds real tables) or convert that page separately.
- **Pixel-perfect facsimile**: output is a clean *re-flow* that keeps images and
  reading order, not a 1:1 copy of the original page layout.
- **Rewriting**: it translates and re-lays-out; it does not summarize, add a TL;DR,
  or editorialize. Faithfulness is the point (see Fidelity below).

## Dependencies

`uv` (runs Python with inline deps), Google Chrome or Chromium (visual
verification). Python packages come via `uv run --with`: PyMuPDF, Pillow, numpy.
Nothing to pre-install beyond Chrome and uv.

## Workflow

Copy this checklist and tick as you go:

```
- [ ] 1. Extract structure + render pages   (extract_pdf.py)
- [ ] 2. Read pages/*.png — SEE the layout, find content vs decorative images
- [ ] 3. (only if translating) run the translation workflow
- [ ] 4. Build the single-file HTML          (build_html.py)
- [ ] 5. Verify visually                      (verify_render.py → Read every segment)
- [ ] 6. Deliver the .html
```

### 1. Extract

```bash
uv run --with pymupdf python scripts/extract_pdf.py input.pdf
```

Writes `input-build/` with `structure.json` (text blocks with font sizes + image
blocks flagged `decorative`), `images/`, and `pages/` (one PNG per page).

### 2. Look before you build

Read `input-build/pages/*.png`. This is not optional: you need to see the real
layout, confirm which images are content vs decoration, and spot tables/charts.
For a long PDF, read every page; for a short one it's quick. This is also where
you understand the document well enough to translate it well.

### 3. Translate (optional)

Only if the user asked for another language. Read
[references/translation_workflow.md](references/translation_workflow.md) and
follow it: a Dynamic Workflow translates pages in parallel, captions data charts,
and reconciles terminology. It produces two overlay files (`units.json`,
`caps.json`) that step 4 consumes. **Do not** hand-translate inline for anything
longer than a page — the workflow keeps terminology consistent and is far faster.

### 4. Build

```bash
# original-language HTML
uv run --with Pillow python scripts/build_html.py input-build/structure.json --out output.html

# translated HTML (overlays from step 3)
uv run --with Pillow python scripts/build_html.py input-build/structure.json --out output.html \
    --translation input-build/units.json --captions input-build/caps.json --lang zh-CN
```

`build_html.py` is **data-driven**: it infers heading levels from font size (most
common size = body; larger steps up to h3/h2/h1), drops decorative images, and
inlines content images as compressed base64 → one portable file. It is not
hand-tuned to any document. If a particular PDF has an unusual structure (e.g.
multi-column, sidebars, a figure the size heuristic misreads), read the script and
adjust — it's short and meant to be edited per document.

### 5. Verify visually (mandatory)

```bash
uv run --with Pillow --with numpy python scripts/verify_render.py output.html
```

Then **Read every `seg-*.png`** and check: fonts render (no tofu boxes), no
clipped tables/figures, headings/lists look right, all expected images present.
Text being correct does not mean the render is correct (failure_cases #7). Fix and
re-verify until it's clean.

A quick structural cross-check is fine too, but count occurrences correctly:
`grep -o '<figure>' output.html | wc -l` — **not** `grep -c` (failure_cases #1).

### 6. Deliver

Hand over the single `.html`. It's self-contained (images inlined), so it opens
with a double-click and nothing can go missing.

## Scripts

| Script | Run with | Purpose |
|--------|----------|---------|
| `scripts/extract_pdf.py` | `uv run --with pymupdf` | PDF → structure.json + images/ + page renders |
| `scripts/build_html.py` | `uv run --with Pillow` | structure.json (+ optional translation/captions) → single-file HTML |
| `scripts/verify_render.py` | `uv run --with Pillow --with numpy` | headless-Chrome render → readable PNG segments |

## Fidelity (read before translating)

The deliverable looks authoritative, so wrong content is worse than ugly content.
The non-negotiable rules — and the specific ways this has gone wrong before — are
in [references/failure_cases.md](references/failure_cases.md). The one that bites
hardest: **never give a real person an inferred translated name, and copy every
number/proper-noun verbatim** (failure_cases #6). Read that file before any
translation run; skim it before any run.

## Next Step

After producing the HTML, suggest the natural follow-up:

```
Conversion complete: output.html (single self-contained file).

Options:
A) Make a PDF of it — run /daymade-docs:pdf-creator if you want a print/share copy (Recommended if they need to send it)
B) Extract the text as Markdown instead — run /daymade-docs:doc-to-markdown (if they wanted editable text, not a reading page)
C) No thanks — the HTML is what I wanted
```

© daymade · MIT · daymade/claude-code-skills

Similar skills

Everything for Writing and docs →

From the same set68

frontend-visual-qa Checks the interface you can actually see in a real browser: typography, overflow, responsive layout, routes, charts. Useful right after UI work, when the build passes but nobody looked. CX Codex +1 572installs benchmark-due-diligence Runs adversarial due diligence on a benchmark that looks inflated, splits marketing bubble from real signal, and maps the playbook onto your own resources. CX Codex +1 483installs bigdata-skill Extracts structured Bigdata.com (RavenPack) financial and news data via SDK and REST API. Built for data pipelines when MCP tear sheets lack machine-readable fields. CX Codex +1 473installs auto-repo-setup Diagnoses and repairs repository setup, startup sync and hooks. Useful when a repo won't run or you need guarded commit and push. CX Codex +1 469installs terminal-screenshot Renders colored terminal output into a PNG image. It lets the agent visually verify syntax themes and contrast instead of guessing from ANSI codes. CX Codex +1 433installs claude-usage-analyst This skill explains where your Claude tokens and money went, reading ccusage data for model mix, cache and 5-hour blocks. CX Codex +1 403installs marketplace-health-check Runs a parallel audit of a skills marketplace repo: script safety, docs, PII, open PRs and issues, manifest integrity. CX Codex +1 389installs llm-eval-harness This skill probes a third-party LLM endpoint across six dimensions: availability, request fidelity, speed, concurrency, protocol compliance and quality regression. CX Codex +1 372installs

Project sponsors

Companies that keep this analytics open
Gold sponsor

Impressions, clicks and code copies — open statistics. We keep the numbers public for the same reason NeBlask itself is public. Become a sponsor