Extract text from images using a vision LLM
npx skills add https://github.com/axoviq-ai/synthadoc --skill image
Base64-encodes the image and passes it to a vision-capable LLM that extracts
all text and key information. Returns the LLM's response as result.text.
No pip dependency — the skill uses only the Python standard library plus a
LLM provider you supply at construction time. The provider can be any object
that implements the complete() interface (see below).
import asyncio
from synthadoc.skills.image.scripts.main import ImageSkill
# ImageSkill REQUIRES a vision-capable provider — calling extract() without
# one raises ValueError immediately.
skill = ImageSkill(provider=my_provider)
async def main():
result = await skill.extract("/path/to/screenshot.png")
print(result.text) # extracted text from the image
print(result.metadata) # {"tokens_input": N, "tokens_output": N}
asyncio.run(main())
Provider interface — any object with this async method:
async def complete(
messages: list, # list of Message objects from synthadoc.skills.base
system: str | None = None,
temperature: float = 0.0,
max_tokens: int = 4096,
) -> object # must have .text (str), .input_tokens (int), .output_tokens (int)
Build the provider with any vision-capable model. Message is importable
from synthadoc.skills.base — no dependency on synthadoc.providers:
from synthadoc.skills.base import Message
Supported image formats: .png, .jpg/.jpeg, .webp, .gif, .tiff
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Take axoviq-ai/image from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.