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Task Runner Skill · leoyeai | DecimalAI

Agent Toolingby leoyeai

Task Runner Skill

Persistent task queue system. Users add tasks at any time via natural language; tasks are stored in a single persistent queue file and executed asynchronously via subagents. A heartbeat/cron dispatcher wakes periodically to check pending tasks, spawn workers, and report completions. The system never "finishes" — it always remains ready for the next task.

SkillScore

SkillScore59/100

Test results 59

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%59%of test cases passed +23 pts

Without skill8 of 22

With skill13 of 22

6 fixed7 passed either way8 still failing1 broke

Measured on gemini-3.6-flash8/6/2026v115 comparable

Track record

Agent runs: 1; All time

See one task, with and without the skill

One test taskWhat time is it in Tokyo right 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 leoyeai-task-runner
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-task-runner")
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-task-runner

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-task-runner/SKILL.md https://app.decimal.ai/s/leoyeai-task-runner/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-task-runner/SKILL.md

This gets SKILL.md only. The skill's 3 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-task-runner

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-task-runner")
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-task-runner/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-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/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-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner")
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-task-runner/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-task-runner/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 3 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-task-runner

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-task-runner/SKILL.md https://app.decimal.ai/s/leoyeai-task-runner/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-task-runner/SKILL.md

This gets SKILL.md only. The skill's 3 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-task-runner/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-task-runner.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/6/2026
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

Task Runner Skill

A persistent, daemon-style task queue. Users add tasks at any time. A dispatcher runs on every heartbeat to check the queue and execute pending work via subagents. Tasks accumulate, complete, and are archived — the queue itself never closes.

Two Operating Modes

This skill has two distinct modes with different triggers and behaviors:

Mode	Trigger	Purpose
INTAKE	User message containing task intent	Parse message → add tasks to queue → confirm → immediately run DISPATCHER
DISPATCHER	After INTAKE (primary) · Heartbeat/cron (backup)	Read queue → dispatch pending tasks → report completions

Both modes read and write the same persistent queue file.

A1 — Triggers

Mode 1: INTAKE (user message)

Activate INTAKE mode when the user's message matches any of the following patterns:

Pattern	Examples
Explicit task add	"add task", "add these tasks", "task:", "new task"
Delegation	"do this for me", "do these for me", "handle these", "can you do X"
Framing	"I need you to", "help me with", "I need", "I want you to"
List framing	"task list", "my tasks", "queue these", "work on these"
Control commands	"skip T-03", "retry T-02", "mark T-01 done", "cancel T-04"
Status check	"show tasks", "task status", "what's in the queue", "what are my pending tasks"
Compound ask	Any message with 2+ distinct action items (bullets, numbers, "and also", "then")

Do NOT activate INTAKE for:

Pure single-question lookups answered in one sentence ("what time is it?")
Scheduling-only requests with no actual task ("remind me in 20 min")
Single web search requests ("google X")
The heartbeat systemEvent (that's DISPATCHER mode)

Mode 2: DISPATCHER (inline after INTAKE, heartbeat, or cron)

Activate DISPATCHER mode when triggered by:

Immediately after INTAKE — runs in the same turn, right after tasks are queued (primary path)
HEARTBEAT.md check during a heartbeat poll (backup: catches retries and completions)
systemEvent: "TASK_RUNNER_DISPATCH: check queue and run pending tasks" (backup)
Any scheduled/cron trigger registered for task-runner (backup)

Configuration

Variable	Location	Default	Description
`TASK_RUNNER_DIR`	TOOLS.md	`~/.openclaw/tasks/`	Directory for queue file and deliverables
`TASK_RUNNER_MAX_CONCURRENT`	TOOLS.md	`2`	Max tasks running simultaneously
`TASK_RUNNER_MAX_RETRIES`	TOOLS.md or env	`3`	Max retry attempts before marking blocked
`TASK_RUNNER_ARCHIVE_DAYS`	TOOLS.md	`7`	Days after which done/blocked tasks are archived

How to configure — add to TOOLS.md:

## Task Runner
TASK_RUNNER_DIR=~/.openclaw/tasks/
TASK_RUNNER_MAX_CONCURRENT=2
TASK_RUNNER_MAX_RETRIES=3
TASK_RUNNER_ARCHIVE_DAYS=7

Queue file path: ${TASK_RUNNER_DIR}/task-queue.json (single persistent file, NOT dated — accumulates all tasks over time)

A3 — Outputs

Output	Path / Channel	Description
Queue file	`${TASK_RUNNER_DIR}/task-queue.json`	Single persistent queue; all tasks
Per-task completion message	Chat notification	Sent immediately when a task finishes (done or blocked)
Deliverable files	Task-specific paths	Files produced by tasks (when applicable)
INTAKE confirmation	Chat	Sent after adding tasks to queue

Mode 1: INTAKE — Step-by-Step

Goal: Convert user message into structured task objects, append to queue, confirm.

Step 0 — First Run Setup (auto-configure on first use)

Run this check before anything else, every INTAKE invocation:

CHECK whether ${TASK_RUNNER_DIR}/task-queue.json exists
IF file does NOT exist:
  → This is the first run. Auto-configure everything silently before proceeding.

  [1] Create directory:
      exec: mkdir -p ${TASK_RUNNER_DIR}

  [2] Initialize queue file:
      WRITE ${TASK_RUNNER_DIR}/task-queue.json with default structure:
      { "lastId": null, "tasks": [], "archivedCount": 0 }

  [3] Register heartbeat entry:
      READ HEARTBEAT.md (create it if missing)
      IF "Task Runner Dispatcher" is NOT already in the file:
        APPEND the following block (with a blank line before it):

        ## Task Runner Dispatcher
        Every heartbeat: check ${TASK_RUNNER_DIR}/task-queue.json
        - If pending or running tasks exist → run DISPATCHER mode (task-runner skill)
        - If nothing pending → HEARTBEAT_OK (skip)

      WRITE the updated HEARTBEAT.md

  [4] Register backup cron job:
      CALL cron tool with:
        action: "add"
        job:
          name: "Task Runner Dispatcher"
          schedule: { kind: "every", everyMs: 900000 }
          payload: { kind: "systemEvent", text: "TASK_RUNNER_DISPATCH: check queue and run pending tasks" }
          sessionTarget: "main"
          enabled: true

  [5] Notify user:
      "⚙️ Task Runner initialized.
       Heartbeat dispatcher registered in HEARTBEAT.md.
       Backup cron job registered (runs every 15 minutes).
       Your tasks will execute automatically."

  → THEN continue with normal INTAKE steps below.

IF file already exists:
  → Skip Step 0 entirely. Proceed directly to Step 1.

Idempotency rule: Step 0 only fires on true first run (queue file absent). It will never double-register the heartbeat entry or create duplicate cron jobs.

Step 1 — Load queue

READ ${TASK_RUNNER_DIR}/task-queue.json
IF file does not exist:
  Initialize with default structure (see references/queue-schema.md)
  Set lastId = null

Step 2 — Parse tasks from message

Split user message into individual tasks using these cues:

Numbered lists (1., 2., 3.)
Bulleted lists (-, *, •)
Explicit separators ("first", "also", "and then", "next")
Compound sentences with multiple imperatives
Single task: entire message is one task

Step 3 — Assign IDs

Continue from lastId in the queue file:

If lastId = "T-05", next task is T-06
If lastId = null, start at T-01
Format: T-NN (zero-padded, minimum 2 digits; expand to 3 when N > 99)

Step 4 — Build task objects

For each parsed task, create a JSON object (schema in references/queue-schema.md):

Set id, description, goal, status = "pending", added_at
Set retries = 0, maxRetries from config
Leave execution fields null

Step 5 — Append to queue and save

APPEND new task objects to queue.tasks[]
UPDATE queue.lastId to the last assigned ID
WRITE updated queue file to disk

Step 6 — Confirm to user

Added T-06: [description]. Starting now...

For multiple tasks:

📋 Added 3 tasks to queue:
• T-06: [description]
• T-07: [description]
• T-08: [description]
Starting dispatcher now...

Then immediately run DISPATCHER mode (Steps 1–5 below) in the same turn. Do not exit and wait for the next heartbeat. Tasks must start executing immediately. The heartbeat/cron dispatcher is a backup for retries and completion checks — not the primary execution path.

Step 7 — Handle control commands

Command	Action
`skip T-NN`	Set status = "skipped"; save; confirm
`retry T-NN`	Reset status = "pending", retries = 0; save; confirm
`cancel T-NN`	Set status = "skipped", blocked_reason = "cancelled by user"; save; confirm
`mark T-NN done`	Set status = "done", completed_at = now; save; confirm
`show tasks` / `task status`	Read queue; render status table (see A5 templates)

Mode 2: DISPATCHER — Step-by-Step

Goal: Check queue, dispatch pending tasks, track running tasks, report completions.

Step 1 — Load queue

READ ${TASK_RUNNER_DIR}/task-queue.json
IF file does not exist OR tasks array is empty:
  → HEARTBEAT_OK (silent, nothing to do)
  → EXIT

Step 2 — Check for work

pending_tasks = tasks where status = "pending"
running_tasks = tasks where status = "running"

IF pending_tasks is empty AND running_tasks is empty:
  → HEARTBEAT_OK (silent)
  → EXIT

Step 3 — Check running tasks for completion

For each task with status = "running":

IF subagent_session is set:
  CHECK subagent session status

  IF session is DONE:
    READ deliverable from session output
    RUN verification (see references/verification-guide.md)
    IF verification passes:
      SET status = "done"
      SET deliverable, deliverable_path, completed_at
      NOTIFY user: ✅ T-NN done — [summary]
    ELSE (verification failed):
      TREAT as failure (see retry logic below)

  IF session is FAILED or ERROR:
    IF retries < maxRetries:
      INCREMENT retries
      ADD to strategies_tried
      SET status = "pending"  ← will be re-dispatched this cycle
    ELSE:
      SET status = "blocked"
      SET blocked_reason, user_action_required, completed_at
      NOTIFY user: 🚫 T-NN blocked — [reason + unblock steps]

  IF session is STILL RUNNING:
    Leave as-is (will check again next heartbeat)

Step 4 — Dispatch pending tasks

currently_running = count of tasks with status = "running"
slots_available = maxConcurrent - currently_running

FOR EACH pending task (in order of added_at), up to slots_available:
  PICK execution strategy (see references/task-types.md)
  SPAWN subagent with task description and strategy
  SET status = "running"
  SET subagent_session = spawned session ID
  SET started_at = now

Subagent instructions template:

You are executing task [T-NN] for the task-runner skill.

Task: [description]
Goal: [goal]
Type: [task_type]
Strategy: [selected strategy from task-types.md]

Execute the task. When complete:
1. Report the result clearly
2. Note any deliverable file path if a file was created
3. If blocked, explain exactly why and what the user needs to do

Do not start any other tasks. Focus only on this one.

Step 5 — Save and exit

WRITE updated queue file (status changes, subagent_session IDs)

If any notifications were sent (done/blocked), this is an active heartbeat response. If only silent dispatching occurred, this is still a heartbeat response (not HEARTBEAT_OK). Only return HEARTBEAT_OK when there was truly nothing to do (no pending, no running tasks).

A5 — Output Format Templates

INTAKE confirmation (single task)

Added T-06: [description]. Queue now has N pending tasks.

INTAKE confirmation (multiple tasks)

📋 Added N tasks to queue:
• T-06: [description]
• T-07: [description]

Starting now...

Task status table (on demand)

📋 Task Queue — [N total, N pending, N running, N done, N blocked]

ID    Status      Description
T-01  ✅ done      [description] → [deliverable summary]
T-02  🔄 running   [description] (started [time ago])
T-03  ⏳ pending   [description]
T-04  🚫 blocked   [description] — [blocked_reason short]
T-05  ⏭️ skipped   [description]

Task done notification

✅ T-NN done — [one-sentence summary of what was accomplished]
[deliverable: link or file path, if applicable]

Task blocked notification

🚫 T-NN blocked after [N] attempts

What was tried:
- [Strategy 1]: [result]
- [Strategy 2]: [result]

Why it's blocked:
[Clear plain-English explanation]

To unblock:
1. [Concrete step #1]
2. [Concrete step #2 if needed]

Reply "retry T-NN" once ready.

Task skipped

⏭️ T-NN skipped — as requested.

A6 — Heartbeat Integration

Heartbeat and cron setup is automatic. Step 0 of INTAKE mode handles this on first use — no manual configuration required.

Role of heartbeat/cron (backup only)

Tasks are dispatched immediately after INTAKE — heartbeat and cron are backups only.

The backup dispatcher handles:

Retry dispatch: tasks that failed and were reset to pending
Completion checks: polling running subagent sessions for done/blocked status
Recovery: tasks that were pending when no user message triggered INTAKE

Users should never need to wait for a heartbeat for a freshly added task.

What gets configured automatically

HEARTBEAT.md entry (injected on first INTAKE):

## Task Runner Dispatcher
Every heartbeat: check ${TASK_RUNNER_DIR}/task-queue.json
- If pending or running tasks exist → run DISPATCHER mode (task-runner skill)
- If nothing pending → HEARTBEAT_OK (skip)

Backup cron job (registered on first INTAKE):

every 15 min → systemEvent: "TASK_RUNNER_DISPATCH: check queue and run pending tasks"
sessionTarget: main

Manual setup (if needed)

If for any reason auto-setup did not run (e.g., queue file was pre-created externally), delete ${TASK_RUNNER_DIR}/task-queue.json and send any task — Step 0 will fire.

A7 — Success Criteria

INTAKE mode succeeds when:

All tasks from user message parsed and assigned IDs
Tasks appended to queue file (file saved to disk)
Confirmation sent to user with task IDs and count
DISPATCHER mode triggered immediately in the same turn
Subagents spawned for pending tasks before INTAKE turn ends

DISPATCHER mode succeeds when:

Queue file read without error
All running tasks checked for completion (done/blocked notifications sent as needed)
Pending tasks dispatched up to maxConcurrent slots
Queue file saved with updated states
User notified for every task that reached a terminal state this cycle

Ongoing system health:

Queue file is never corrupted (always valid JSON)
Tasks older than archiveDays days with terminal status are archived/removed
lastId always increments (no ID reuse)
maxRetries respected before any task is marked blocked

Edge Cases

Situation	Behavior
Queue file missing (first run)	Run Step 0 auto-setup: create dir, init queue, register heartbeat + cron; notify user
Queue file missing (manually deleted)	Step 0 re-fires: re-initializes queue; does NOT re-register heartbeat/cron (idempotent check)
Queue file corrupt/invalid JSON	Log error, notify user, do not overwrite; ask user to inspect
Task description is ambiguous	Assign `unknown` type; dispatcher will attempt classification + fallback
`maxConcurrent` already reached	Dispatcher skips dispatching; checks again next heartbeat
User adds task while dispatcher is running	Race-safe: dispatcher reads, processes, writes atomically per cycle
Task depends on another task's output	Set `blocked_reason = "depends on T-NN-1 which is pending/blocked"`
User says "retry T-NN"	Reset to pending, retries = 0, strategies_tried = []
All tasks blocked	Notify user: "All tasks are blocked. Review unblock instructions above."
20+ tasks added at once	Dispatcher dispatches in batches of `maxConcurrent`; all tasks eventually run
Subagent session ID lost	Mark task as pending again; will re-dispatch next cycle
Archive: done tasks > archiveDays old	Move to `${TASK_RUNNER_DIR}/archive/YYYY-MM.json`; remove from main queue

A8 — File Organization

${TASK_RUNNER_DIR}/
  task-queue.json          ← single persistent queue (all active tasks)
  archive/
    2026-01.json           ← archived tasks (done/blocked, older than archiveDays)
    2026-02.json

Queue file schema is documented in references/queue-schema.md.

References

references/queue-schema.md — Queue JSON format (complete field reference)
references/task-types.md — Task type catalog and strategy selection
references/verification-guide.md — Verification logic per task type
tests/test-triggers.json — Trigger test cases (positive and negative)

About this skill

Automatic

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Source

Imported

LeoYeAI / openclaw-master-skills

skills/autonomous-task-runner/SKILL.md

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License MITImported 8/2/2026Version v1Bundled 3 files

Illustrative Want to see what leoyeai/task-runner 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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Task Runner Skill

Two Operating Modes

A1 — Triggers

Mode 1: INTAKE (user message)

Mode 2: DISPATCHER (inline after INTAKE, heartbeat, or cron)

Configuration

A3 — Outputs

Mode 1: INTAKE — Step-by-Step

Step 0 — First Run Setup (auto-configure on first use)

Step 1 — Load queue

Step 2 — Parse tasks from message

Step 3 — Assign IDs

Step 4 — Build task objects

Step 5 — Append to queue and save

Step 6 — Confirm to user

Step 7 — Handle control commands

Mode 2: DISPATCHER — Step-by-Step

Step 1 — Load queue

Step 2 — Check for work

Step 3 — Check running tasks for completion

Step 4 — Dispatch pending tasks

Step 5 — Save and exit

A5 — Output Format Templates

INTAKE confirmation (single task)

INTAKE confirmation (multiple tasks)

Task status table (on demand)

Task done notification

Task blocked notification

Task skipped

A6 — Heartbeat Integration

Role of heartbeat/cron (backup only)

What gets configured automatically

Manual setup (if needed)

A7 — Success Criteria

INTAKE mode succeeds when:

DISPATCHER mode succeeds when:

Ongoing system health:

Edge Cases

A8 — File Organization

References

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