Marketplace file-converter

This skill handles file format conversions across documents (PDF, DOCX,

install

source · Clone the upstream repo

git clone https://github.com/aiskillstore/marketplace

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiskillstore/marketplace "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/89jobrien/file-converter" ~/.claude/skills/aiskillstore-marketplace-file-converter && rm -rf "$T"

manifest: skills/89jobrien/file-converter/SKILL.md

File Converter

Overview

Convert files between formats across three categories: documents, data files, and images. Generate Python code dynamically for each conversion request, selecting appropriate libraries and handling edge cases.

Conversion Categories

Documents

From	To	Recommended Library
Markdown	HTML	`markdown` or `mistune`
HTML	Markdown	`markdownify` or `html2text`
HTML	PDF	`weasyprint` or `pdfkit` (requires wkhtmltopdf)
PDF	Text	`pypdf` or `pdfplumber`
DOCX	Markdown	`mammoth`
DOCX	PDF	`docx2pdf` (Windows/macOS) or LibreOffice CLI
Markdown	PDF	Convert via HTML first, then to PDF

Data Files

From	To	Recommended Library
JSON	YAML	`pyyaml`
YAML	JSON	`pyyaml`
JSON	CSV	`pandas` or stdlib `csv` + `json`
CSV	JSON	`pandas` or stdlib `csv` + `json`
JSON	TOML	`tomli` / `tomllib` (read) + `tomli-w` (write)
XML	JSON	`xmltodict`
JSON	XML	`dicttoxml` or `xmltodict.unparse`

Images

From To Recommended Library

PNG/JPG/WebP/GIF

Any raster

Pillow

(PIL)

SVG

PNG/JPG

cairosvg

svglib

reportlab

PNG

SVG

potrace

(CLI) for tracing, limited fidelity

Workflow

Identify source format (from file extension or user statement)
Identify target format
Check
```
references/
```
for format-specific guidance
Generate conversion code using recommended library
Handle edge cases (encoding, transparency, nested structures)
Execute conversion and report results

Quick Patterns

Data: JSON to YAML

import json
import yaml

with open("input.json") as f:
    data = json.load(f)

with open("output.yaml", "w") as f:
    yaml.dump(data, f, default_flow_style=False, allow_unicode=True)

Data: CSV to JSON

import csv
import json

with open("input.csv") as f:
    reader = csv.DictReader(f)
    data = list(reader)

with open("output.json", "w") as f:
    json.dump(data, f, indent=2)

Document: Markdown to HTML

import markdown

with open("input.md") as f:
    md_content = f.read()

html = markdown.markdown(md_content, extensions=["tables", "fenced_code"])

with open("output.html", "w") as f:
    f.write(html)

Image: PNG to WebP

from PIL import Image

img = Image.open("input.png")
img.save("output.webp", "WEBP", quality=85)

Image: SVG to PNG

import cairosvg

cairosvg.svg2png(url="input.svg", write_to="output.png", scale=2)

Resources

Detailed guidance for complex conversions is in

references/

```
references/document-conversions.md
```
- PDF handling, encoding issues, styling preservation
```
references/data-conversions.md
```
- Schema handling, type coercion, nested structures
```
references/image-conversions.md
```
- Quality settings, transparency, color profiles

Consult these references when handling edge cases or when the user has specific quality/fidelity requirements.