/image
Extract text from images using a vision LLM
$ npx -y skills add axoviq-ai/synthadoc --skill image --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/image
Context preview
The summary Claude sees to decide when to auto-load this skill.
Extract text from images using a vision LLM
SKILL.md
image.SKILL.mdname: image
version: "1.0"
description: Extract text from images using a vision LLM
entry:
script: scripts/main.py
class: ImageSkill
triggers:
extensions:
- ".png"
- ".jpg"
- ".jpeg"
- ".webp"
- ".gif"
- ".tiff"
intents:
- "image"
- "screenshot"
- "diagram"
- "photo"
requires: [] # no pip packages; a vision LLM provider must be passed at construction time
author: axoviq.com
license: AGPL-3.0-or-laterImage Skill
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`.
Setup
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).
Standalone usage
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`
When this skill is used
- Source path ends with `.png`, `.jpg`, `.jpeg`, `.webp`, `.gif`, or `.tiff`
- User intent contains: `image`, `screenshot`, `diagram`, `photo`
Read more
name: image
version: "1.0"
description: Extract text from images using a vision LLM
entry:
script: scripts/main.py
class: ImageSkill
triggers:
extensions:
- ".png"
- ".jpg"
- ".jpeg"
- ".webp"
- ".gif"
- ".tiff"
intents:
- "image"
- "screenshot"
- "diagram"
- "photo"
requires: [] # no pip packages; a vision LLM provider must be passed at construction time
author: axoviq.com
license: AGPL-3.0-or-laterImage Skill
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`.
Setup
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).
Standalone usage
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`
When this skill is used
- Source path ends with `.png`, `.jpg`, `.jpeg`, `.webp`, `.gif`, or `.tiff`
- User intent contains: `image`, `screenshot`, `diagram`, `photo`
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