Securityby leoyeai

HaS Privacy

HaS (Hide and Seek) on-device text and image anonymization. Text: 8 languages (zh/en/fr/de/es/pt/ja/ko), open-set entity types. Image: 21 privacy categories (face, fingerprint, ID card, passport, license plate, etc.).

SkillScore

SkillScore68/100

Test results 68

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

24%68%of test cases passed +44 pts

Without skill6 of 25

With skill17 of 25

11 fixed6 passed either way8 still failing0 broke

Measured on gemini-3.6-flash8/8/2026v116 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI want to run text hide on a single file `memo.txt` to replace person names and output to `memo_clean.txt`. What parameter is strictly required for single-file anonymization mapping?

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 leoyeai-has-anonymizer
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("leoyeai-has-anonymizer")
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 leoyeai-has-anonymizer

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/leoyeai-has-anonymizer/SKILL.md https://app.decimal.ai/s/leoyeai-has-anonymizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-has-anonymizer/SKILL.md

This gets SKILL.md only. The skill's 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/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/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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("leoyeai-has-anonymizer")
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/leoyeai-has-anonymizer/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-has-anonymizer/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 6 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 leoyeai-has-anonymizer

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/leoyeai-has-anonymizer/SKILL.md https://app.decimal.ai/s/leoyeai-has-anonymizer/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-has-anonymizer/SKILL.md

This gets SKILL.md only. The skill's 6 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/leoyeai-has-anonymizer/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-has-anonymizer.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: LeoYeAI/openclaw-master-skills ↗
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

HaS Privacy

HaS exposes a single umbrella CLI:

has text ... for text anonymization, restoration, and scanning
has image ... for image scanning, masking, and category discovery

Use it when you need to remove private data locally before sending content elsewhere, inspect a directory for privacy risks, or mask visual privacy targets in photos and screenshots.

Agent Decision Guidelines

Prefer has text for plaintext and has image for raster images. For mixed directories, run both and combine the results into one report.
For PDFs, Word documents, or scanned pages, extract text first and then use has text. For screenshots/photos where the goal is simply to hide visible carriers such as faces, screens, paper, labels, or QR codes, use has image. If the goal is to reason about the text content inside an image, run OCR first and then use has text.
Do not overwrite or delete the original files. Text commands can restore later, image masking is irreversible.
Proactively mention configurable knobs when the user intent is clear: has text uses repeated --type; has image uses repeated --type, plus --method and --strength.
If the user intent is ambiguous, start with scan before hide.
After batch scans, summarize text file count, image file count, findings by type/category, high-risk items, and the suggested next step.
If timing matters to the user, add --timing and report the elapsed result in plain language afterward.
For qr_code and barcode, the default mosaic strength is automatically raised based on the detection size to ensure the encoding is destroyed. The agent does not need to manually increase --strength for these categories. If a detection output includes effective_strength, report it to the user.

Shared CLI Contract

The current CLI contract is designed for agents first:

Success returns compact JSON.
Failure also returns compact JSON with error.code and error.message.
Returned path fields are absolute.
- This includes file, output, mapping_output, and skipped[].file.
Invalid combinations fail fast instead of silently falling back.
Directory mode is non-recursive. Only immediate children are processed.
Batch results can include skipped and skipped_count.
- Treat skipped entries as unprocessed files, not as clean files.

Shared command layout:

{baseDir}/scripts/has.sh <text|image> <command> [options]

Shared options can be placed before or after the subcommand.

Part 1: `has text`

has text is the plaintext namespace. It supports:

scan
hide
restore

It runs entirely on-device and uses a local llama-server plus the HaS text model when model inference is required.

Core Text Concepts

Semantic tags

Anonymized text uses semantic tags such as:

<EntityType[ID].Category.Attribute>

This preserves structure better than a flat [REDACTED] token and is the reason restored downstream LLM output can remain usable.

Open-set types

Repeated --type flags are open-set. They are not limited to a fixed catalog. Natural language type names such as "person name", "address", "phone number", or "numeric values (transaction amounts)" are valid.

Public/private distinction

Type wording matters. For example, "personal location" is usually safer than "location" if you want to preserve public places but hide private addresses. Public/private person-name distinctions remain less stable and should not be trusted without verification.

Multilingual support

The text model supports Chinese, English, French, German, Spanish, Portuguese, Japanese, and Korean, including mixed-language text.

Type name language

Match the --type language to the source text language:

Chinese text → use Chinese type names: --type "人名" --type "电话号码" --type "地址"
Non-Chinese text (English, French, German, etc.) → use English type names: --type "person name" --type "phone number" --type "address"

Text Runtime Prerequisites

has text auto-starts a local llama-server when needed.

Default model path: ~/.openclaw/tools/has-anonymizer/models/has_text_model.gguf
Override model path: HAS_TEXT_MODEL_PATH=/abs/path/to/has_text_model.gguf
Override parallel cap: HAS_TEXT_MAX_PARALLEL_REQUESTS
If HuggingFace downloads fail, see Model Download Mirrors below.

Text Usage

{baseDir}/scripts/has.sh text [--timing] [--verbose] <scan|hide|restore> [options]

Namespace options:

Option	Description
`--timing`	Include `elapsed_ms` in the JSON output
`--verbose`	Emit runtime status and progress messages to stderr

Input methods:

Method	Description
`--text '<text>'`	Pass text directly
`--file <path>`	Read text from a file
`--dir <path>`	Process immediate plaintext files in a directory
stdin	For single-text mode when no `--text`, `--file`, or `--dir` is provided

Rules:

--text, --file, and --dir are mutually exclusive.
Empty --type values are rejected.
Directory mode only accepts batch output flags.
Single-file hide requires --mapping-output.
Single-file restore requires --mapping.
In text directory mode, skipped can include unprocessed files (binary, encoding, or read errors).

`has text scan`

Finds sensitive entities without replacing them.

{baseDir}/scripts/has.sh text scan --type "person name" --type "phone number" --file report.txt
{baseDir}/scripts/has.sh text scan --type "person name" --type "phone number" --dir ./reports/

Parameters:

Parameter	Required	Description
`--type`	yes	Entity type to scan for; repeat to add more
`--text` / `--file` / `--dir`	one input	Input source
`--max-chunk-tokens`		Max tokens per chunk, default `5000`
`--max-parallel-requests`		Max scan chunks in parallel, default `4`

Output:

Single-text mode returns {"entities": ...}
Directory mode returns {"results":[...],"count":N,"summary":{...}}
Batch output may include skipped and skipped_count

`has text hide`

Replaces sensitive entities with semantic tags.

{baseDir}/scripts/has.sh text hide --type "person name" --type "address" --text "John lives in Brooklyn" --mapping-output ./mapping.json
{baseDir}/scripts/has.sh text hide --type "person name" --file note.txt --output ./note.anonymized.txt --mapping-output ./note.mapping.json
{baseDir}/scripts/has.sh text hide --type "person name" --dir ./docs/

Parameters:

Parameter	Required	Description
`--type`	yes	Entity type to anonymize; repeat to add more
`--text` / `--file` / `--dir`	one input	Input source
`--mapping-output`	single-file: yes	Output path for generated mapping JSON
`--output`	single-file	Output path for anonymized text
`--mapping`	single-file	Existing mapping JSON file for incremental anonymization
`--output-dir`	batch	Output directory for anonymized files (default: `<dir>/.has/anonymized/`)
`--mapping-dir`	batch	Output directory for per-file mapping JSON files (default: `<output-dir>/mappings/`)
`--max-chunk-tokens`		Max tokens per chunk, default `3000`
`--max-parallel-requests`		Max files in parallel for `--dir`, default `4`
`--no-tool-pair`		Disable diff-based pair extraction; always use Model-Pair (slower but more robust)

Behavior:

Single-file mode never emits the mapping table inline.
Single-file mode returns either:
- {"text":"...","mapping_output":"/abs/path/to/map.json"}
- {"output":"/abs/path/to/out.txt","mapping_output":"/abs/path/to/map.json"}
Batch mode does not accept shared --mapping.
Mapping files are sensitive assets. Protect them.

`has text restore`

Restores anonymized text using mapping JSON.

{baseDir}/scripts/has.sh text restore --mapping mapping.json --text "<person name[1].personal.name> lives in ..."
{baseDir}/scripts/has.sh text restore --mapping mapping.json --file anonymized.txt --output restored.txt
{baseDir}/scripts/has.sh text restore --dir ./.has/anonymized/ --output-dir ./.has/restored/

Parameters:

Parameter	Required	Description
`--mapping`	single-file: yes	Mapping JSON file path
`--text` / `--file` / `--dir`	one input	Input source
`--output`	single-file	Output path for restored text
`--mapping-dir`	batch	Per-file mapping directory (default: `<dir>/mappings/`)
`--output-dir`	batch	Output directory for restored files (default: sibling `restored/` under `.has/`, or `<dir>/.has/restored/`)
`--max-chunk-tokens`		Max tokens per chunk when model restore is needed, default `3000`
`--max-parallel-requests`		Max model-backed restore chunks in parallel

Behavior:

Single-file mode returns inline text unless --output is provided.
restore --dir uses per-file mapping JSON files. It does not accept a shared --mapping.
restore --dir expects mapping files at <mapping-dir>/<filename>.mapping.json (matching the naming convention produced by hide --dir).

Typical Text Workflow

Anonymize text before sending it to a cloud LLM, then restore the answer:

hide to produce anonymized text plus mapping
send anonymized text to the cloud model with a tag-format explanation (see below)
restore the model response with the mapping

For multi-line text, prefer file-based intermediates over shell variables.

Prompting the cloud LLM with anonymized text

When forwarding anonymized text to a cloud LLM, the agent must prepend a brief explanation of the tag format so the model understands and preserves the tags. Include wording equivalent to the following (adjust language to match the conversation):

The text below has been anonymized. Sensitive entities are replaced by tags in the format <EntityType[ID].Category.Attribute>:

EntityType — the kind of entity (matches the --type value, e.g. person name, address, phone number).

[ID] — a numeric identifier. The same type + same ID always refers to the same real-world entity (e.g. every <person name[1]> is the same person; <person name[2]> is a different person).

.Category.Attribute — additional semantic classification of the entity.

Rules:

Preserve every tag exactly as-is in your response — do not modify, translate, paraphrase, omit, or expand any tag.

When referring to an anonymized entity, reuse the original tag with the correct ID.

Do not attempt to guess the real values behind the tags.

Omitting this explanation may cause the cloud model to strip, rewrite, or misinterpret the tags, which will break the restore step.

Model Download Mirrors

If HuggingFace downloads fail, use these ModelScope mirrors:

text model: https://modelscope.cn/models/TencentXuanwu/HaS_Text_0209_0.6B_Q8
image model: https://modelscope.cn/models/TencentXuanwu/HaS_Image_0209_FP32

Part 2: `has image`

has image is the image namespace. It supports:

scan
hide
categories

It loads the YOLO segmentation model directly and does not require llama-server.

Image Usage

{baseDir}/scripts/has.sh image [--timing] [--model MODEL] <scan|hide|categories> [options]

Namespace options:

Option	Applies to	Description
`--timing`	all image commands	Include `elapsed_ms` in the JSON output
`--model PATH`	`scan`, `hide`	Override the image model path

Image Privacy Categories

Common categories include biometric_face, id_card, passport, license_plate, qr_code, mobile_screen, and paper.

Use has image categories when you need the full catalog of 21 supported classes.

--type accepts:

English names
Chinese names
numeric IDs
unique partial matches such as face

Rules:

Empty --type values are rejected.
Ambiguous partial matches fail fast.
Omit --type to scan or mask all supported categories.
In image directory mode, skipped can include unprocessed files.

`has image scan`

Finds privacy regions without modifying the image.

{baseDir}/scripts/has.sh image scan --image photo.jpg --type face --type id_card
{baseDir}/scripts/has.sh image scan --dir ./photos/ --type face

Parameters:

Parameter	Required	Description
`--image` / `--dir`	one input	Single image or batch directory
`--type`		Category filter; repeat to add more
`--conf`		Confidence threshold, default `0.25`
`--model`		Override image model path

Output:

Single-image mode returns detections and summary
Directory mode returns results, count, summary, and optional skipped

`has image hide`

Detects and masks privacy regions in images.

{baseDir}/scripts/has.sh image hide --image photo.jpg --type face --method blur --strength 25
{baseDir}/scripts/has.sh image hide --dir ./photos/

Parameters:

Parameter	Required	Description
`--image` / `--dir`	one input	Single image or batch directory
`--output`	single-image	Output image path
`--output-dir`	batch	Output directory
`--type`		Category filter; repeat to add more
`--method`		`mosaic`, `blur`, or `fill`; default `mosaic`
`--strength`		Mosaic block size or blur radius; default `15`
`--fill-color`		Fill color for `fill`; default `#000000`
`--conf`		Confidence threshold; default `0.25`
`--model`		Override image model path

Behavior:

Refuses to overwrite the source image.
Directory mode accepts --output-dir, not --output.
For qr_code and barcode detections with --method mosaic, the block size is automatically raised to max(strength, bbox_short_side // 10, 20) to prevent the encoding from surviving pixelation. After masking, a lightweight verification confirms the code is no longer machine-readable; if it is, the strength is escalated further (up to a fill fallback). Each affected detection includes an effective_strength field in the output.
A cv2-based fallback supplements YOLO detection for QR codes and barcodes. When YOLO misses a code (e.g. large codes on plain backgrounds), cv2.QRCodeDetector and cv2.barcode.BarcodeDetector provide additional coverage. When YOLO misclassifies a code region as a different category (e.g. monitor_screen), cv2 corrects the category before --type filtering, so --type qr_code catches all QR codes regardless of YOLO's label. Corrected detections include a "corrected_from" field; new detections include "cv2_fallback": true.

`has image categories`

Lists all supported image privacy categories.

{baseDir}/scripts/has.sh image categories
{baseDir}/scripts/has.sh image categories --timing

Behavior:

Returns {"categories":[...]}
Supports --timing

Suggested Combined Scan

For a mixed workspace:

run has text scan ... --dir <dir> for plaintext
run has image scan --dir <dir> for images
merge the two JSON results into one privacy report

If the user wants masking after that, use hide on the specific files or directories you already identified.

When it activates

Automatically, when a task matches. Use when: (1) anonymizing text before sending to cloud LLMs then restoring the response, (2) anonymizing documents, code, emails, or messages before sharing, (3) scanning text or images for sensitive content, (4) anonymizing logs before handing to ops/support, (5) masking faces/IDs

About this skill

Automatic

Security & AppSec pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

View 15 scanner checks

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

LeoYeAI / openclaw-master-skills

skills/has-anonymizer/SKILL.md

View on GitHub ↗

License MITImported 8/2/2026Version v1Bundled 6 files · scripts

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

Related skills

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

From the same source and nearby in the catalogue.

SkillScore

Safety

What it changes

Track record

Use this skill

HaS Privacy

Agent Decision Guidelines

Shared CLI Contract

Part 1: has text

Core Text Concepts

Semantic tags

Open-set types

Public/private distinction

Multilingual support

Type name language

Text Runtime Prerequisites

Text Usage

has text scan

has text hide

has text restore

Typical Text Workflow

Prompting the cloud LLM with anonymized text

Model Download Mirrors

Part 2: has image

Image Usage

Image Privacy Categories

has image scan

has image hide

has image categories

Suggested Combined Scan

When it activates

SkillSafety

Source

Related skills

More in the registry

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

HaS Privacy

Agent Decision Guidelines

Shared CLI Contract

Part 1: has text

Core Text Concepts

Semantic tags

Open-set types

Public/private distinction

Multilingual support

Type name language

Text Runtime Prerequisites

Text Usage

has text scan

has text hide

has text restore

Typical Text Workflow

Prompting the cloud LLM with anonymized text

Model Download Mirrors

Part 2: has image

Image Usage

Image Privacy Categories

has image scan

has image hide

has image categories

Suggested Combined Scan

When it activates

SkillSafety

Source

Related skills

More in the registry

Part 1: `has text`

`has text scan`

`has text hide`

`has text restore`

Part 2: `has image`

`has image scan`

`has image hide`

`has image categories`

Part 1: `has text`

`has text scan`

`has text hide`

`has text restore`

Part 2: `has image`

`has image scan`

`has image hide`

`has image categories`