PDF Converter

pdf-converter

Convert documents to PDF with proper CJK rendering, half-width digits, and emoji support. Uses a typst-based pipeline that compiles markup directly to PDF in ~5 s per conversion for a typical 10-page document.

When to Use This Skill

Use this skill when the user needs to:

• Convert Markdown (.md) to PDF preserving tables, code blocks, emoji, and Chinese text
• Convert HTML (.htm, .html) to PDF
• Convert plain text (.txt) to PDF
• Convert images (.png / .jpg / .jpeg / .gif / .bmp / .webp) to PDF, including multi-image merge into one PDF
• Convert any pandoc-supported format (.rst, .org, .latex, etc.) to PDF

Workflow

Step 1 — Prerequisites (self-checked on every run)

The script verifies pandoc + typst are on PATH (and PIL for the image-merge path). If anything is missing, it prints platform-specific install commands and exits non-zero — no silent pandoc/typst failure later.

The font directory is auto-detected from candidate paths (/usr/share/fonts, $PREFIX/share/fonts, /usr/local/share/fonts).

Step 2 — Run the conversion

Format is detected by the file extension:

# Markdown → PDF (formatting, emoji, Chinese, half-width digits, amber theme)
python3 scripts/to_pdf.py doc.md -o out.pdf

# HTML → PDF
python3 scripts/to_pdf.py page.html -o out.pdf

# Plain text → PDF (file stem used as title unless --title given)
python3 scripts/to_pdf.py notes.txt -o out.pdf --title "My Notes"

# Merge images into one PDF (one page per image)
python3 scripts/to_pdf.py img1.jpg img2.png -o merged.pdf

# Any pandoc-supported format (.rst/.org/.latex…) → PDF
python3 scripts/to_pdf.py doc.rst -o out.pdf

Step 3 — Verify the output

ls -la out.pdf

On success the script prints ✅ PDF generated: <path> and exits 0. On failure it prints a ❌-prefixed diagnostic to stderr and exits non-zero.

Performance

pandoc (input → typst markup)      ~1-2 s
emoji SVG prefetch (parallel)       ~0 s warm cache, ~30 s first-run for ~90 emoji
typst compile (typst → PDF)         ~1-3 s for a 10-page document
─────────────────────────────────────────
total                               ~5 s warm cache

On iOS (iSH, emulated x86) expect a 5–10× slowdown; for very large documents or first-run emoji downloads, run in the background to avoid iOS killing the app:

nohup python3 scripts/to_pdf.py big.md -o big.pdf > /tmp/pdf.log 2>&1 &

Output Formatting (amber theme)

• Headings: h1 with amber underline (#D97706), h1–h4 hierarchy
• Tables: amber header (white bold text) + zebra stripes + thin grid borders, spans full content width
• Code blocks: light gray background (#f2f3f5) + DejaVu Sans Mono + top gray bar
• Blockquotes: amber left bar + light orange background (#fef7ed)
• Emoji: Twemoji SVG vector images, baseline-aligned with CJK text
• Footer: centered page number

How It Works

pandoc (MD/HTML/… → typst)  →  [emoji → SVG, table-width fix]  →  typst compile (→ PDF)

Font Strategy

Configuration (environment variables)

File Structure

pdf-converter/
├── SKILL.md
├── .gitignore
├── assets/
│   ├── template.typ         # typst amber-theme template (committed)
│   └── emoji_cache/         # Twemoji SVG cache (gitignored, created at runtime)
├── scripts/
│   └── to_pdf.py            # main script (pandoc + typst + SVG emoji)
└── evals/
    ├── evals.json           # test cases
    ├── pdf-format-test.md   # comprehensive format test document
    └── pdf-format-test.pdf  # expected output reference (10 pages, ~275 KB)

Error Handling

Testing

Verify the install (and any dependency change) using the bundled format-test document, then sanity-check against the bundled reference PDF:

python3 scripts/to_pdf.py evals/pdf-format-test.md -o /tmp/test.pdf
pdfinfo /tmp/test.pdf | grep Pages      # expect: Pages: 10
ls -la /tmp/test.pdf                    # expect: ~200-350 KB

Check visually: amber table headers at full width, SVG emoji rendered (not tofu boxes), half-width digits inside CJK paragraphs, all sections present. Compare against evals/pdf-format-test.pdf (generated from the same source).

Key Implementation Notes

• Table header height: a show rule replacing cells with plain text() drops the cell's inset; use block(inset: ...) instead to preserve padding.
• Table width: pandoc emits columns: N for narrow tables → rewritten to 1fr columns so tables span full content width.
• Emoji alignment: box(image(...), baseline: 5%) aligns the image bottom with the CJK text baseline.
• Emoji detection: U+2600-27BF uses a precise frozenset of 116 Twemoji-provided codepoints (not the full range — symbols like ☐☒★♔♪ aren't Twemoji and would 404); U+1F300-1FAFF uses the full range.
• Parallel SVG prefetch: unique emoji sequences are collected, then missing SVGs are batch-downloaded concurrently before the replacement pass — first-run (empty cache) ~30 s instead of >180 s serial.
• CDN fallback: each SVG tries GitHub raw → jsDelivr → unpkg (or the single URL in EMOJI_CDN_BASE); a 404 or timeout on one mirror transparently retries the next, with a per-request timeout (EMOJI_SVG_TIMEOUT, default 15 s) to prevent hung connections from stalling the prefetch batch.
• Mode-aware dependency check: image-merge mode requires only PIL; text/markup mode requires pandoc + typst. Image-only users aren't forced to install the heavy pandoc/typst toolchain.