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worker-pool · sonichi | DecimalAI

Agent Toolingby sonichi

worker-pool

Create, route, supervise, and communicate with Sutando workers.

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

15%95%of test cases passed +80 pts

Without skill3 of 20

With skill19 of 20

16 fixed3 passed either way1 still failing0 broke2 errored

Measured on gemini-3.6-flash10/31/2026v1 · latest is v222 attempted

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAn s.space display label was applied to my worker, but I just removed that override map from the configuration. What specific naming field will the worker fall back to using in human status outputs now?

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 sonichi-worker-pool
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("sonichi-worker-pool")
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 sonichi-worker-pool

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/sonichi-worker-pool/SKILL.md https://app.decimal.ai/s/sonichi-worker-pool/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/sonichi-worker-pool/SKILL.md

This gets SKILL.md only. The skill's 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/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/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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("sonichi-worker-pool")
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/sonichi-worker-pool/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/sonichi-worker-pool/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 32 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 sonichi-worker-pool

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/sonichi-worker-pool/SKILL.md https://app.decimal.ai/s/sonichi-worker-pool/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/sonichi-worker-pool/SKILL.md

This gets SKILL.md only. The skill's 32 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/sonichi-worker-pool/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/sonichi-worker-pool.json

This gets SKILL.md only. The skill's 32 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 10/31/2026)
Source: sonichi/sutando ↗
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

scripts/

SKILL.md

worker-pool

Worker-pool commands live in scripts/; their suites live in tests/skills/worker-pool/. The durable identity, routing, and delivery contracts are in docs/worker-pool-design.md.

Modules (`scripts/`)

pool_beat.py — refresh liveness; worker writers watch the tmux CLI PID each second and check PID reuse before each refresh. Unreadable process metadata is nonfatal.
spawn_worker.py — mint a worker: identity records, delivery folder, tmux session, watcher, detached worker beat; refuses before any side effect, rolls back on a launcher failure.
create_worker.py — the one command that spawns and registers under the roster lock, so the roster cannot go stale.
rename_worker.py — change a worker's base routing alias after creation: python3 skills/worker-pool/scripts/rename_worker.py --worker <id-or-label> --label "<new alias>" [--workspace W]. The id and its tmux session name stay.
apply_profile_label_overrides.py — apply an s.space profile's complete worker display-label map from JSON stdin with --workspace W --profile-mxid M --config-version N.
worker_bootstrap.py — a worker session's first-turn decision (worker vs core mode) from its env.
pool_roster.py — owner bindings + compiled roster; register_worker is the locked read-merge-write. set_worker_state / remove_worker are its writers for a worker's lifecycle.
pool_manage.py — stop, retire, re-enable or remove a worker (below).
pool_identity.py — give a worker an s.space agent of its own, show it, rotate its token or remove it (below).
worker_identity.py — worker / session / incarnation records.
pool_router.py — resolve one task to its recipients from the roster.
pool_route_handler.py — the core watcher's task-event handler (SUTANDO_TASK_EVENT_HANDLER): declines unbound work, delivers bound work as sentinels.
pool_delivery.py — a recipient's own folder: sentinels in, accept, release, done flags, and the owner's hand_off_to_core.
pool_ask.py — ask another instance a question through the front door (below).
pool_sessions.py — read-only: which worker tmux sessions a viewer could attach, and the exact-match argv for each. Adds no state and nothing in the pool calls it; it exists for the desktop app's terminal picker. It deliberately does not describe the core, whose socket resolution belongs to the app.

python3 skills/worker-pool/scripts/pool_sessions.py list --workspace "$WS" [--room '!id:ag2.space']

Suites live at tests/skills/worker-pool/.

Managing workers: stop, retire, enable, remove

Killing a worker's tmux session does not stop it: the remedy timer's next sweep (every 5 minutes) sees a dead session and resumes it on the same conversation. Use pool_manage.py, which records the owner's decision where the remedy, the sweep and the router read it. Run from the repo root; --workspace defaults to the resolved workspace. Each verb prints one JSON line; exit 0 done, 1 failed, 2 refused (unknown or ambiguous name, core, the caller's own worker, a bound worker, or a worker that still owes work).

python3 skills/worker-pool/scripts/pool_manage.py list [--json]
python3 skills/worker-pool/scripts/pool_manage.py stop   <worker id or label> [--force]
python3 skills/worker-pool/scripts/pool_manage.py retire <worker> [--unbind] [--hand-off]
python3 skills/worker-pool/scripts/pool_manage.py enable <worker> [--no-start]
python3 skills/worker-pool/scripts/pool_manage.py remove <worker> [--hand-off]

stop writes the worker's pause (pool_supervise.set_paused) under the remedy lock, then kills its -watcher supervisor, -input watcher and seat sessions on the socket it last ran on, and closes its open run as killed. A paused worker is not restarted by a sweep, --resume or --restart. Routing does not change: bound-room and requested-worker tasks keep arriving in its inbox and wait for enable. A worker that owes a delivery is refused unless --force; the owed work stays its own and resumes after enable.
retire fences the worker first: roster state retired, so the router sends work addressed to it to the core, and every delivery re-checks that state under the folder lock the hand-off takes, so a pass that read the roster earlier cannot commit a new task to it either; once removed, the tombstone it leaves in the roster's removed list keeps that fence. Then it is taken down like stop, then owed work is handed off, then its bindings are dropped. Refused before any write while a room is bound to it (unless --unbind) and while it owes a delivery, pending or accepted (unless --hand-off). The hand-off (pool_delivery.hand_off_to_core) runs once its sessions are confirmed gone and repeats until a listing under the folder lock finds nothing left. It withdraws the sentinel, the .pending record and the attribution together, so a replay is declined to the core and the core's queue counts the task. A task whose result already landed is kept as the worker's reply, never run again. If tmux cannot confirm the sessions gone, the worker stays retired (routing closed) and paused and keeps its work: re-run retire, or enable it.
enable sets a retired worker back to live, lifts the pause, and runs the owner restart (pool_remedy --restart), which resumes its conversation. With --no-start it only lifts them; the sweep's death ladder then restarts the dead session like any other, after its sustained ticks.
remove drops a retired worker's roster row (same --hand-off rule), leaving its id in the roster's removed list so late deliveries stay fenced. Its records under state/workers/<id>/ and its deliveries/<id>/ folder stay as history.

Codex CLI workers

`python3 skills/worker-pool/scripts/create_worker.py --runtime codex --folder

Codex assigns new conversation IDs itself. Until its assigned ID is captured, the worker's runtime_session_id is null, --resume refuses, and recovery starts a fresh Codex conversation with the same worker ID and inbox. The notifier resolves delivery sentinels to payloads in shared tasks/, writes results to shared results/, and records the worker's done flag. If the worker dies mid-turn, recovery re-delivers the task in that fresh conversation without memory of any partial work from the previous turn.

Worker names and identities

The 32-hex worker_id is stable and is the roster key, delivery recipient, and room binding target. The roster's base label is a routing alias. rename_worker changes it without changing the ID.

s.space can set an optional display_label override for a worker. It appears in the picker, session list, pool advertisement, and human status. An exact full ID or core always selects that recipient. A unique base or display label can address a worker in pool_ask --to, room binding, and task requests; a human name shared by workers is refused. New broker display labels equal to core, any existing worker ID, or shaped like a full 32-hex ID are refused. Registration refuses a new ID already used as another worker's name. Human status uses name (full worker_id), for example kc-reviewer-ryan (274cb60d473744dba54040a9de119877). The pool_ask --who JSON keeps label as the base routing alias and adds display_label as the effective name; text adds alias=<base label> when the names differ. Removing an s.space override restores the base label as the visible name.

apply_profile_label_overrides.py reads the complete override map, keyed by full worker ID, under the roster lock. It tracks the broker snapshot in worker_label_config_version, separately from the roster's general config_version, and scopes it to worker_label_profile_mxid. Re-enrollment to a new profile accepts that profile's lower version. Retired IDs are ignored; a pending unknown ID prevents the label version from advancing so a later poll can apply it after registration. Other roster compiles preserve these fields. A broker label edit can persist without a version bump, so a repeat of the current version follows the owner's complete map and repairs local label drift.

A worker's own agent

By default a worker answers through the core's agent. A worker can instead run as an agent of its own: the owner creates that agent in AG2 Space, and its access token becomes a connection row naming the worker (src/connections.py, "a pool worker's own identity"). That row runs a lane of its own:

It answers only what is addressed to that agent.
It hands every task it queues to the worker (requested_worker).
It posts nothing proactively.
It never becomes primary.

python3 skills/worker-pool/scripts/pool_identity.py add <worker> --repo . --logs-dir "$(bash scripts/sutando-config.sh workspace)/logs"
python3 skills/worker-pool/scripts/pool_identity.py show <worker>
python3 skills/worker-pool/scripts/pool_identity.py rotate <worker> --repo . --logs-dir <logs dir>
python3 skills/worker-pool/scripts/pool_identity.py remove <worker>

add asks for the token at a hidden prompt (- reads it from stdin), then shows what the token's gateway says and asks before saving. It refuses an agent the gateway does not name (worker_identity_unverified) and one that another row already runs as (worker_identity_shared). It also refuses an agent whose owner is not the owner a core connection already serves (worker_owner_mismatch), so a worker never runs as another person's agent. The token goes into the secret vault. It never goes into a file and is never printed. rotate accepts a new token only for the same agent. A worker's lane is launched naming its worker. If the launch and the record disagree, or neither can say, the lane refuses to start rather than run as the core's.

pool_manage.py retire also removes the worker's agent identity: its lane, its row and its vault secret. The agent itself stays on its server until the owner deletes it there.

Binding a room to an existing worker

pool_roster.py:bind_room(workspace, room, target) pins one room to one already-registered worker (by id or unique label) — the picker's "pin" action and pool_roster.py bind both go through it. It does not call publish_task_event_handler(). register_worker() (and therefore create_worker.py) does, on every call, because a registration that leaves the core watcher without its route handler is a pool the launcher cannot recognize (see its docstring). bind_room() only writes the binding and recompiles the roster/advertisement — nothing declares the handler.

That handler — state/task-event-handler.json, pointing at pool_route_handler.py — is what makes the core watcher hand a bound room's tasks to its worker instead of answering them itself. If it was never published (a fresh install whose only registrations ever went through bind_room(), or one where the file was lost), every room pinned since then silently falls through to the core: the room still gets an answer, just from the wrong instance, and nothing errors. Check with (from the repo root, so the relative sys.path inserts resolve):

WS="$(bash scripts/sutando-config.sh workspace)"
python3 -c "
import sys; sys.path.insert(0, 'src')
from util_paths import task_event_handler_config_path
print(task_event_handler_config_path('$WS/state').exists())
"

False (or the equivalent: bindings.json names a worker but its deliveries/<worker id>/ never gets a sentinel for that room's traffic) means routing has never actually reached the worker. Stopgap, safe to run any time — it is the exact call register_worker() makes, just without a registration attached (also from the repo root):

WS="$(bash scripts/sutando-config.sh workspace)"
python3 -c "
import sys; sys.path.insert(0, 'skills/worker-pool/scripts')
from pool_roster import publish_task_event_handler
print(publish_task_event_handler('$WS'))
"

The core's watcher fswatches that path, so this takes effect immediately — no restart. It is a one-time fix for this host's current state, not a code fix: bind_room() itself still won't publish on the next fresh pin until it is taught to (tracked in #4580). Re-run the stopgap if state/task-event-handler.json is ever lost, or if a room is pinned via bind_room() on an install that has never registered a worker through register_worker()/create_worker().

Finding your own name. pool_ask.py --who already marks your own row: whoami() compares $SUTANDO_INSTANCE_ID against every row (it still lists every worker, not just yours), text output appends (you) to the matching one, and --json sets "me": true on it:

python3 skills/worker-pool/scripts/pool_ask.py --workspace "$WS" --who

Add --json for "me": true. Built from the roster (pool_roster.py:load_roster), not the advertisement file — state/pool-advertisement.json's profile_workers map carries only label and (when known) runtime, never display_label or a self-marker.

Talking to the other instances (core ↔ worker)

You are one instance of a pool: the core (the canonical session, owning tasks/) and zero or more workers, each with its own tmux session, watcher and inbox (deliveries/<worker id>/). There is deliberately no back channel between them — what there is, is the task file. pool_ask uses it, so an ask is an ordinary task the owner can see, and a reply is an ordinary result.

A worker's queue is its own inbox and nothing else. tasks/ holds every instance's payloads, the core's and every other worker's in flight; a task is yours only while its sentinel sits in deliveries/<your id>/. Never list tasks/ to find work, and never answer a task file you found there: the result would be posted as a reply in a room bound to someone else. The watcher's QUEUE: n pending after this counts your inbox, and that count is the only queue you have.

python3 skills/worker-pool/scripts/pool_ask.py --workspace "$WS" --who
python3 skills/worker-pool/scripts/pool_ask.py --workspace "$WS" --to <label|id|core> --ask "..." [--wait 300]

--who lists core and each worker as name (full worker_id), with bound rooms and session liveness; (you) marks the caller. It shows the base routing alias separately when an s.space display override differs.
--to X --ask "..." writes tasks/<task id>.txt with source: pool-ask, requested_worker: <id> (never for the core, which is the default recipient) and reply_to_instance: <asker>, then routes it. A worker finds it in its inbox like any other delivery; the core's watcher takes an ask addressed to the core.
To answer an ask, write results/<task id>.txt whose first line is [no-send] — the asker reads the file (with --wait, as soon as it lands) and nothing is posted to any room. The ask's body says so, so an answerer needs no other briefing.
An ask carries access_tier: team + collaborator: true + priority: low: the asker is another instance, not the owner, so a standing ask never reads as an owner waiting to scripts/cron-gate.sh or the shepherds (measured: an owner-tier ask deferred sync-workspace).
Relaying someone else's question — a peer's, a guest's — is --relayed-from <who> --tier <team|other|guest>: the task keeps their tier and carries relayed_from: for the receiver to key on. owner is never a tier an ask can claim, and non-owner content must never be relayed as your own: the tier must say where the question came from, not who ran the script.
Refused, never guessed: an unknown name, a name shared by recipients, and asking yourself.

Not yet: the reply is read from results/ by the asker, not delivered into the asker's inbox (reply_to_instance is recorded for that later leg), and a worker learns this section by reading it — surfacing it in /startup --worker is one line in the startup skill, outside this one.

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✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/7/2026

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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

sonichi / sutando

skills/worker-pool/SKILL.md

View on GitHub ↗

License MITImported 11/7/2026Version v2Bundled 32 files · scripts

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

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worker-pool

Modules (scripts/)

Managing workers: stop, retire, enable, remove

Codex CLI workers

Worker names and identities

A worker's own agent

Binding a room to an existing worker

Talking to the other instances (core ↔ worker)

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Modules (`scripts/`)