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vision-skills · anionex | DecimalAI

Agent Toolingby anionex

vision-skills

Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image).

Maintained

SkillScore

SkillScore89/100

Test results 89

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

63%89%of test cases passed +26 pts

Without skill12 of 19

With skill17 of 19

6 fixed11 passed either way1 still failing1 broke3 errored

Measured on gemini-3.6-flash10/10/2026v222 attempted

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe want to inspect a tiny checkbox in `settings_page.png` to see if it is checked. Since a full-image vision pass might miss tiny details, what is the recommended two-step command sequence using local CLI tools?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add anionex-vision-skills
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install anionex-vision-skills

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/anionex-vision-skills/SKILL.md https://app.decimal.ai/s/anionex-vision-skills/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/anionex-vision-skills/SKILL.md

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add anionex-vision-skills

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add anionex-vision-skills

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add anionex-vision-skills

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add anionex-vision-skills

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add anionex-vision-skills

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add anionex-vision-skills

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add anionex-vision-skills

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add anionex-vision-skills

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add anionex-vision-skills

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add anionex-vision-skills

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add anionex-vision-skills

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add anionex-vision-skills

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("anionex-vision-skills")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/anionex-vision-skills/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add anionex-vision-skills

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/anionex-vision-skills/SKILL.md https://app.decimal.ai/s/anionex-vision-skills/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/anionex-vision-skills/SKILL.md

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/anionex-vision-skills/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/anionex-vision-skills.json

This gets SKILL.md only. The skill's 10 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 10/10/2026
Source: anionex/agent-vision-toolkit ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

vision-skills

Five local CLIs that give a text-only agent eyes. They read one shared vision config (VISION_API_KEY / VISION_BASE_URL / VISION_MODEL / LANG), plus the optional Python-client settings VISION_API_PROTOCOL, VISION_REASONING_EFFORT, and VISION_USER_AGENT — no extra credentials.

Pick the tool by the question you are answering:

Question	Tool
"What does this image show / say?"	`glance`
"Where is X?" — a thing you can name	`ground`
"Where are all the Xs?" — every instance of a kind	`detect`
"What is its exact shape, size, offset?"	`trace`
"Cut this box out as its own image file"	`crop`
"OCR this long screenshot / scrolling page / chat history"	`scripts/long_screenshot_ocr.py`
"Extract the icon/logo foreground as transparent PNG — manual region or auto (cropped+scaled screenshots)"	`scripts/extract_fg.py`
"Turn this HTML file into a viewport or full-page screenshot"	`scripts/html_shot.py`
"Which colours dominate a region, and which palette value fits it?"	`scripts/dominant_colors.py`
A relation none of them return — a gap, a distance between two located things	code over the pixels (Pillow)

glance answers what something is; ground and detect answer where. You give ground a description of a particular thing; you give detect a kind and it enumerates the instances.

Both give real coordinates, but they are not pixel-exact: the box arrives on a 0-1000 grid and is scaled to your image, so the last pixel or few are not reliable. That is accurate enough to crop with, to click, to compare positions against. When a number has to be exact, trace derives it from the actual pixels — offsets, sizes, shapes.

Use the provided tools before hand-rolled pixels

Everything this toolkit ships a tool for, call the tool — do not rewrite it with Pillow in the middle of a task. The CLIs exist so the same pixel work is not hand-coded differently every time:

cut a box out of an image → crop, not Image.open(...).crop(...)
sample a region's palette → scripts/dominant_colors.py
compare two images → scripts/pixel_diff.py
vectorize to SVG → trace
locate / inventory elements → ground / detect
describe / OCR an image → glance
safely split, OCR, and merge a long screenshot → scripts/long_screenshot_ocr.py
HTML file to a viewport or full-page screenshot → scripts/html_shot.py

Hand-written Pillow is only for what none of them return: a relation between two things you already located (a gap, a distance), a resize or overlay, drawing. If you catch yourself writing .crop(), .convert(), or histogram code where one of the tools above fits, replace it with the tool call — same coordinates, same box format, and the output feeds the next tool directly.

glance — ask about an image

glance <image>                                 # detailed description
glance <image> -q "<question>"                 # targeted question (qualitative only)
glance <image> --ocr                           # verbatim OCR
glance <image> --region X1,Y1,X2,Y2 -q "..."   # zoom into a crop
glance <img1> <img2> -q "..."                  # compare in ONE call

When you do compare with glance, pass all paths to one call — separate calls cannot see both images, so two descriptions compared afterwards are two hallucination surfaces, not a comparison. --region uploads only the crop, so small text and icons become readable.

But "what changed between these two?" is not a glance question. A one-word badge or a small shift is a rounding error to a vision model and exact to scripts/pixel_diff.py. Diff first to get the box, then glance --region that box to read what the change actually is.

For a tall scrolling screenshot, do not send the whole image through one OCR call and accept the model's downscaling loss. Run the long-screenshot workflow, which finds low-content cut bands, invokes glance on each chunk, uses structured extraction for chat histories, merges only duplicated overlap, and writes a boundary audit:

python3 scripts/long_screenshot_ocr.py work/page.png -o work/page.ocr.md
python3 scripts/long_screenshot_ocr.py work/chat.png --mode chat --resume -o work/chat.ocr.md

Read references/long-screenshot-ocr.md before using it. It defines the verification pass for unsafe cuts and chat-message boundaries.

ground — locate a named target

ground <image> "<target description>"
ground <image> "<target>" --region X1,Y1,X2,Y2

Output: x1: .., y1: .., x2: .., y2: .. in original-image pixels — with --region too (crop hits are mapped back).

Provider-native 0-1000 boxes do not all use the same array order: Gemini uses [y0, x0, y1, x1], while Qwen3-VL, Qwen3.5, and Qwen3.6 use [x0, y0, x1, y1]. Grounding code must select the order by model family (or an explicit override) before scaling to pixels; never parse every provider as Gemini-style yxyx.

If several boxes come back numbered, your description matched more than one element rather than picking out a single thing. Narrow it with what distinguishes the one you mean — its text, its position, the block it sits in — and ask again.

The box is a handle, not just an answer — it feeds the next call:

$ ground screenshot.png "the send button"
x1: 1067, y1: 841, x2: 1108, y2: 881
$ glance screenshot.png --region 1067,841,1108,881 -q "is it enabled or greyed out?"

That two-step is how you inspect anything too small to survive a full-image pass.

detect — find every instance of a kind

detect <image>                        # every UI element
detect <image> "buttons"              # one kind only
detect <image> --region X1,Y1,X2,Y2   # inside one box
detect <image> "text" --fail-on-empty # fail if no text is detected

The default category targets UI screenshots. For photographs or video frames, pass an explicit category such as "objects" or "text". An empty inventory normally prints no elements detected and exits 0; --fail-on-empty instead exits 1 with a stderr diagnostic and no stdout. This reports an empty model result, not proof that the image has no matching elements.

You name a particular thing for ground; you name a kind for detect and it enumerates the instances. Output is a numbered list with each item's visible text and box. A full-screen pass is a fast first draft — counts vary run to run on dense screens. For completeness, detect the layout blocks first, then detect --region each block.

trace — exact shape geometry (local, no vision API)

trace <image>                                  # b/w spline SVG to stdout
trace <image> --polygon                        # boxy diagrams/wireframes
trace <image> --region X1,Y1,X2,Y2 -o out.svg  # crop first

Coordinates come from the actual pixels, not a model's estimate. Flat, high-contrast graphics only; text becomes curves (pair with --ocr when the text matters). Small images are upscaled automatically before tracing, so a 30px icon traces as readily as a screenshot — size is not a reason to skip the tool. Before shipping or reusing a traced SVG, read references/restore-graphic.md — it holds the reuse traps and the ship-vs-hand-write call.

crop — cut a pixel box out of an image (local, no vision API)

crop <image> --region X1,Y1,X2,Y2             # writes <image-stem>.crop.png next to the input
crop <image> --region X1,Y1,X2,Y2 -o out.png
crop <image> --region X1,Y1,X2,Y2 --scale 4   # upscale the cut-out 4x (LANCZOS) first

The same X1,Y1,X2,Y2 pixel boxes ground/detect print, clamped to the image bounds. Once a box is worth keeping — the same crop is about to feed pixel_diff, dominant_colors, and trace in turn — cut it to a file once and reuse it, instead of re-cropping in memory on every call. --scale N upscales the cut-out before writing (default output name becomes <image-stem>[email protected]): for icons too small for ground/trace to see clearly, crop with --scale 4, then run ground/trace on the upscaled file — coordinates it returns are in the upscaled grid, divide by N to map back to the original image. Requires the optional pillow.

extract_fg — icon foreground as transparent PNG: manual region or auto (local, no vision API)

# manual: you know the region (and optionally the background colour)
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 -o icon.png
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 --mode dark          # grey/black line logos
python3 scripts/extract_fg.py shot.png --region X1,Y1,X2,Y2 --exclude-color '#E6E6E6'
# auto: `crop --scale` cut-outs with the icon centred — no region needed
crop shot.png --region X1,Y1,X2,Y2 --scale 4 -o d/icon1.png
python3 scripts/extract_fg.py d/icon1.png d/icon2.png       # writes <stem>.clean.png next to each input
python3 scripts/extract_fg.py d/icon1.png --disc-radius 60
python3 scripts/extract_fg.py d/icon1.png --boxes "101,84,184,171"

Manual mode keeps every sufficiently large connected component of the region (separate logo sub-shapes stay together; specks drop out). Auto mode takes a crop --scale cut-out with the icon centred (disc + glyph): the disc centre is the image centre, the disc radius defaults to min(w,h)/2 * 0.6, and the disc colour is sampled from a ring around the centre; that colour is excluded and the glyph is picked as the most saturated among the three largest coloured components (white rings, ripples, and text fall away), output as a 1:1 transparent PNG. When auto inference fails, override the radius with --disc-radius, or pass a ground box (in the upscaled grid) as --boxes to recentre and re-filter by overlap. Multiple images may be passed at once (auto mode). Requires the optional pillow (and numpy for auto mode).

html_shot — render an HTML file to an image (local, needs a Chrome-family browser)

python3 scripts/html_shot.py page.html                      # writes page.png, 1280x800
python3 scripts/html_shot.py page.html --width 1440 --height 900 -o page.png
python3 scripts/html_shot.py page.html --scale 2            # 2x pixels: small text stays readable
python3 scripts/html_shot.py page.html --full-page           # complete scroll height, same layout viewport
python3 scripts/html_shot.py page.html --full-page --max-pixels 40000000

The visual-alignment loop: write HTML, screenshot it at the reference viewport, then compare it with the design. Use pixel_diff to locate material differences, not to chase a zero-difference score. Rendering happens in headless Chrome/Chromium/Edge — no Python dependencies. The default captures only the viewport. Use --full-page for the complete document while keeping --width and --height as the layout viewport, so vh/svh and responsive breakpoints do not change. Add --max-pixels N when the page height is untrusted. --wait-ms N pauses for fonts, images, or animation before capturing. Paths are relative to this skill's own directory.

pixel_diff — where two images differ (local, no vision API)

python3 scripts/pixel_diff.py <a> <b>      # path is relative to this skill dir

Prints an overall difference percentage plus the worst regions as x1: .. boxes you can feed straight into glance --region. Exact where a vision model rounds off.

dominant_colors — a region's palette, and the exact value among candidates (local, no vision API)

python3 scripts/dominant_colors.py <image> --region X1,Y1,X2,Y2          # top colour clusters + shares
python3 scripts/dominant_colors.py <image> --region X1,Y1,X2,Y2 \
  --candidates '#F9FAFA,#F5F5F5,#F3F3F3,#EDEDED'                        # pick the best candidate

A vision model names a colour ("light gray") but not its value. The first mode downsamples, quantizes, and merges near-duplicates to list the region's significant colours with the share each owns — the histogram shows which colour is the background and which is the accent. Given the candidate palette your label implies, the second mode scores each candidate by how close the region's pixels are to it and prints the winner. Take the value from here, never from glance's prose. Paths are relative to this skill's own directory.

Work from a copy, not a temp path

If the image lives in a temp directory, before your first tool call on one, copy it somewhere durable and run everything against the copy — that is what keeps the image reachable later:

cp "<the temp path>" work/shot.png
glance work/shot.png -q "..."

Exception: the user asked for the image to stay in a temp folder.

When you have a description instead of the image

If an image reached you only as text — a description written by a person, a tool, or another model — and the image's file path is visible in the conversation, do not reason past a missing detail. Look again yourself:

glance <path> -q "<the specific detail>" — one qualitative follow-up.
ground <path> "<target>" then glance <path> --region <that box> -q "..." — locate, then zoom. The reliable way to inspect one element closely.

If the file no longer exists, say so instead of guessing.

Coarse to fine — the method behind every task above

For a single question about an image, glance is the whole answer. For anything multi-step, work outside-in:

One full-image pass (glance, or a description you already have) for the layout and an inventory of what is where.
For any element that matters, ground it, then zoom with glance --region <box> -q "...". Full-image passes routinely miss small text and icons; a crop puts all the pixels on one detail, so the model sees it at effectively higher resolution. When the same box will be checked more than once, cut it to a file first with crop.
Never take a prose answer for a pixel-level fact — exact colors, small offsets, sizes. Vision models confidently report styling that is not there: coloured syntax highlighting in a monochrome code block, a border that does not exist. Get the number from trace, from a ground box, or from pixel_diff; sample the pixels yourself only for what those cannot return.

Use cases

Each file below is one job, start to finish: when it applies, the call sequence, and how to tell you got it right.

The job	Read
OCR a long screenshot, scrolling page, or chat history without losing text at chunk boundaries	`references/long-screenshot-ocr.md`
Rebuild a page or component as HTML/CSS, including a roughly three-minute fast approximation mode, or align an existing UI with its reference image	`references/restore-ui.md`
Extract or rebuild an icon, logo, illustration, or other isolated graphic as transparent PNG/SVG	`references/restore-graphic.md`
Turn a sketch, diagram, or whiteboard into Mermaid, Graphviz, or another structured representation	`references/restore-structure.md`
Operate a GUI from screenshots — locate, act, verify each step	`references/gui.md`

Notes

Only PNG / JPEG / GIF / WebP images are supported.
If a command is not found, the optional tools were not installed — report this to the user instead of improvising a replacement.
If the vision API fails, relay the error faithfully; never fabricate image content.

Source repository: https://github.com/Anionex/agent-vision-toolkit

Installation guide: https://github.com/Anionex/agent-vision-toolkit/blob/main/AGENT_INSTALL.md

When it activates

Automatically, when a task matches. Use for any task involving an image — questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots — a

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 10/9/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

anionex / agent-vision-toolkit

skills/vision-skills/SKILL.md

View on GitHub ↗

License MITImported 10/9/2026Version v2Bundled 10 files · scripts

Illustrative Want to see what anionex/vision-skills would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

anionex/vision-toolsLocal vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image). Use for any task involving an image — questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots — aClose match

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

vision-skills

Use the provided tools before hand-rolled pixels

glance — ask about an image

ground — locate a named target

detect — find every instance of a kind

trace — exact shape geometry (local, no vision API)

crop — cut a pixel box out of an image (local, no vision API)

extract_fg — icon foreground as transparent PNG: manual region or auto (local, no vision API)

html_shot — render an HTML file to an image (local, needs a Chrome-family browser)

pixel_diff — where two images differ (local, no vision API)

dominant_colors — a region's palette, and the exact value among candidates (local, no vision API)

Work from a copy, not a temp path

When you have a description instead of the image

Coarse to fine — the method behind every task above

Use cases

Notes

When it activates

SkillSafety

Source

Other skills for the same job

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