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file-converter

This skill handles file format conversions across documents (PDF, DOCX, Markdown, HTML, TXT), data files (JSON, CSV, YAML, XML, TOML), and images (PNG, JPG, WebP, SVG, GIF). Use when the user requests converting, transforming, or exporting files between formats. Generates conversion code dynamically based on the specific request.

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包含的文件（5）

SKILL.md3.5 KB
references/data-conversions.md3.2 KB
references/document-conversions.md2.7 KB
references/image-conversions.md4.0 KB
skill-report.json40.0 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

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` or `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.

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