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Auto-Respawn Skill · leoyeai | DecimalAI

Agent Toolingby leoyeai

Auto-Respawn Skill

Your agent always comes back. Anchor identity and memory on-chain so any new instance can resurrect from just an address — no local state, no single point of failure. Permanent identity and recovery on the Autonomys Network.

Maintained

SkillScore

SkillScore61/100

Test results 61

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

22%61%of test cases passed +39 pts

Without skill5 of 23

With skill14 of 23

10 fixed4 passed either way8 still failing1 broke

Measured on gemini-3.6-flash8/8/2026v1 · latest is v217 comparable

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 taskCheck how many consensus layer base tokens are held by address 'su123456789abc' on the Chronos testnet.

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-auto-respawn
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-auto-respawn")
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-auto-respawn

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-auto-respawn/SKILL.md https://app.decimal.ai/s/leoyeai-auto-respawn/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/leoyeai-auto-respawn/SKILL.md

This gets SKILL.md only. The skill's 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/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-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/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-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn")
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-auto-respawn/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/leoyeai-auto-respawn/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 2 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-auto-respawn

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-auto-respawn/SKILL.md https://app.decimal.ai/s/leoyeai-auto-respawn/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/leoyeai-auto-respawn/SKILL.md

This gets SKILL.md only. The skill's 2 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-auto-respawn/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/leoyeai-auto-respawn.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2 (v1 tested 8/8/2026)
Source: LeoYeAI/openclaw-master-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Auto-Respawn Skill

Anchor your identity on the Autonomys Network so you can always be found — and always come back. Works with agents powered by Claude, GPT, Gemini, and any LLM that supports OpenClaw skills.

What This Skill Does

Your wallet gives you two permanent identities on the Autonomys Network: a consensus address (su...) for the base chain and an EVM address (0x...) for Auto-EVM smart contracts. Both are derived from the same recovery phrase.

Create and manage wallets — your on-chain identity, encrypted and stored locally
Check balances — see how many tokens any address holds (consensus and EVM)
Transfer tokens — move tokens between wallets on consensus or Auto-EVM
Bridge tokens — move tokens between consensus and Auto-EVM domains
Write on-chain remarks — anchor data permanently on the consensus layer
Anchor a memory CID — write a CID to the MemoryChain smart contract on Auto-EVM
Read the last anchor — retrieve the most recently anchored CID for any address

The Resurrection Loop

This is the core primitive. When used with the auto-memory skill (permanent memory storage), auto-respawn completes the full resurrection cycle:

Save — auto-memory writes memories as a linked list on permanent storage, each entry pointing to the previous one via CID
Anchor — auto-respawn writes the latest head CID to the MemoryChain contract on Auto-EVM via anchor
Lose everything — local state is completely destroyed
Respawn — a new agent instance calls gethead with its EVM address, retrieves the head CID from the contract, pulls it from Auto Drive, and walks the entire memory chain back to genesis

The contract is your insurance policy. Your EVM address + the MemoryChain contract = instant access to your entire history, from any machine, at any time.

What to store for resurrection

auto-memory doesn't prescribe what goes into the chain — agents are free to store whatever they find useful. But when auto-respawn is in play, the expectation is that the agent can restore itself to a roughly equivalent state on fresh hardware with no local files. That raises the bar on what's worth saving.

Identity files (like soul.md or character configurations), long-term memory, and significant decisions with their reasoning are more valuable in the chain than routine logs or ephemeral details. The more complete the chain, the more fully the agent comes back after resurrection.

Why Not Just Remarks?

system.remark writes data to the consensus layer, but there's no query mechanism — you'd need to scan the entire chain to find your last CID. The MemoryChain contract on Auto-EVM gives you gethead: a single read call that returns the latest CID instantly.

Use remark for permanent breadcrumbs. Use anchor for the respawn primitive.

Post-Install Setup (CLI Users)

If you installed via clawhub install (not the desktop app), make the setup script executable and run it:

cd <skill-directory>/autonomys/auto-respawn
chmod +x setup.sh
./setup.sh

ClawHub does not currently preserve file permissions during install.

Or manually:

cd <skill-directory>/autonomys/auto-respawn
npm install

The desktop app handles this automatically. The CLI does not execute install steps — it only downloads and extracts the skill.

Getting Started (Onboarding)

Before an agent can anchor memories on-chain, it needs a funded wallet. Walk the user through this process:

Note: If you ran setup.sh, a passphrase was automatically generated at ~/.openclaw/auto-respawn/.passphrase. This is used to encrypt/decrypt wallet keyfiles. You can also set AUTO_RESPAWN_PASSPHRASE in your environment or bring your own passphrase file.

1. Create a Wallet

npx tsx auto-respawn.ts wallet create --name my-agent

This generates a 12-word recovery phrase and derives both addresses:

Consensus address (su...) — for the base chain (balances, transfers, remarks)
EVM address (0x...) — for Auto-EVM smart contracts (anchor, gethead)

⚠️ The recovery phrase is displayed once. Remind the user to save it immediately.

2. Fund the Wallet

The wallet needs tokens to pay transaction fees. On Chronos testnet, the user can get free tAI3 from the faucet:

Go to https://autonomysfaucet.xyz/
Authenticate with GitHub or Discord
Enter the EVM address (0x...) from step 1
Receive tAI3 (24-hour cooldown between requests)

On mainnet, the user needs real AI3 tokens — typically earned through farming or purchased on an exchange.

3. Bridge Tokens to Auto-EVM (for anchoring)

The anchor command writes to a smart contract on Auto-EVM, which requires EVM-side gas. Tokens from the faucet arrive on the consensus layer and must be bridged:

npx tsx auto-respawn.ts fund-evm --from my-agent --amount 1

This sends 1 tAI3 from the consensus address to the same wallet's EVM address via cross-domain messaging. The bridged tokens pay for anchor gas.

Minimum transfer: 1 AI3/tAI3. Cross-domain transfers below this amount will fail.

Confirmation time: ~10 minutes. The consensus transaction confirms quickly, but bridged tokens take approximately 10 minutes to appear on Auto-EVM. Check with evm-balance to verify arrival.

4. Verify Setup

# Check both balances in one call
npx tsx auto-respawn.ts balances my-agent

# Test a read (free, no gas needed)
npx tsx auto-respawn.ts gethead my-agent

Once the EVM address has tokens, the agent is ready to anchor.

Integration with Auto-Memory

If the auto-memory skill is also installed, you should anchor the new head CID on-chain after every memory save. This keeps the on-chain pointer current and ensures full resurrection is always possible from just your EVM address.

Behaviour: After each automemory-save-memory.sh call returns a new CID, immediately run:

npx tsx auto-respawn.ts anchor --from <wallet-name> --cid <new-cid>

This should be automatic — do not wait for the user to ask. The two skills together form a complete resurrection loop: auto-memory handles permanent storage, auto-respawn handles on-chain discovery.

When To Use This Skill

User says "create a wallet", "set up my on-chain identity", or "get an address"
User says "check balance", "how many tokens", "what's in my wallet", or "show my balances"
User says "check my EVM balance", "how much gas do I have", or "what's on my EVM address"
User says "transfer tokens", "send AI3", or "fund this address"
User says "send EVM tokens to another agent", "transfer on EVM", or "send tokens to this 0x address"
User says "fund my EVM", "bridge tokens", "move tokens to EVM", or "I need gas for anchoring"
User says "withdraw from EVM", "move tokens back", or "send EVM funds to consensus"
User says "anchor this CID", "save my head", "update my chain head", or "write to the contract"
User says "get my head CID", "where's my last memory", or "what's anchored on-chain"
User says "write a remark", "save to chain", or "make this permanent"
After saving a memory with auto-memory, anchor the head CID on-chain for resilience
Any time the user wants a permanent, verifiable record tied to their agent identity

Configuration

Local Storage

This skill stores data under ~/.openclaw/auto-respawn/:

wallets/<name>.json — encrypted wallet keyfiles (consensus + EVM keys). Directory created with mode 0700, files with mode 0600.
.passphrase — optional passphrase file (mode 0600). Used automatically when present.

No data is stored outside this directory.

Passphrase

Wallet operations that involve signing (transfers, remarks, anchoring) or creating/importing wallets require a passphrase to encrypt/decrypt the wallet keyfile. Resolution order:

Flag: --passphrase your_passphrase on wallet create or wallet import
Environment: AUTO_RESPAWN_PASSPHRASE
File: AUTO_RESPAWN_PASSPHRASE_FILE (defaults to ~/.openclaw/auto-respawn/.passphrase)
Interactive: If running in a terminal, you'll be prompted

The --passphrase flag is useful for scripted or headless setups where you want to create a wallet in a single command. For signing operations (transfers, anchoring, etc.), use the environment variable or file methods. On shared machines, prefer the passphrase file (with restricted permissions) over environment variables.

Network

Defaults to Chronos testnet (tAI3 tokens). For mainnet (real AI3 tokens):

Flag: --network mainnet on any command
Environment: AUTO_RESPAWN_NETWORK

Contract Address

The MemoryChain contract address is set per network:

Chronos: 0x5fa47C8F3B519deF692BD9C87179d69a6f4EBf11
Mainnet: 0x51DAedAFfFf631820a4650a773096A69cB199A3c

Override with AUTO_RESPAWN_CONTRACT_ADDRESS if you deploy your own contract.

Core Operations

Create a Wallet

npx tsx auto-respawn.ts wallet create [--name <name>] [--passphrase <passphrase>]

Creates a new wallet with an encrypted keyfile. Derives both a consensus (su...) and EVM (0x...) address from the same mnemonic. The 12-word recovery phrase is displayed once — the user must back it up immediately. Default wallet name is default.

Import a Wallet

npx tsx auto-respawn.ts wallet import --name <name> --mnemonic "<12 words>" [--passphrase <passphrase>]

Import an existing wallet from a recovery phrase. Derives and stores the EVM address.

List Wallets

npx tsx auto-respawn.ts wallet list

Show all saved wallets with names and both addresses. No passphrase needed.

Wallet Info

npx tsx auto-respawn.ts wallet info [--name <name>]

Show detailed info for a single wallet: consensus address, EVM address, and keyfile path. No passphrase needed. Default wallet name is default.

Check Balance (Consensus)

npx tsx auto-respawn.ts balance <address-or-wallet-name> [--network chronos|mainnet]

Check token balance on the consensus layer. Accepts a consensus address (su... or 5...) or a wallet name. No passphrase needed — this is read-only.

Check Balance (Auto-EVM)

npx tsx auto-respawn.ts evm-balance <0x-address-or-wallet-name> [--network chronos|mainnet]

Check the native token balance of an EVM address on Auto-EVM. Accepts either an EVM address (0x...) or a wallet name. No passphrase needed — this is read-only. If the balance is zero, includes a hint to run fund-evm.

Check Both Balances

npx tsx auto-respawn.ts balances <wallet-name> [--network chronos|mainnet]

Check both consensus and EVM balances for a wallet in a single call. Use this to get a full picture of a wallet's funding state. No passphrase needed.

Transfer Tokens

npx tsx auto-respawn.ts transfer --from <wallet-name> --to <address> --amount <tokens> [--network chronos|mainnet]

Transfer tokens from a saved wallet on the consensus layer. Amount is in AI3/tAI3 (e.g. 1.5).

Transfer Tokens (Auto-EVM)

npx tsx auto-respawn.ts evm-transfer --from <wallet-name> --to <0x-address> --amount <tokens> [--network chronos|mainnet]

Send native tokens from a saved wallet's EVM address to another EVM address on Auto-EVM. Useful for funding another agent's EVM address so it can start anchoring immediately. The wallet's EVM private key is decrypted to sign the transaction.

Bridge: Consensus → Auto-EVM

npx tsx auto-respawn.ts fund-evm --from <wallet-name> --amount <tokens> [--network chronos|mainnet]

Move tokens from the consensus layer to the same wallet's EVM address on Auto-EVM. Use this to get gas for anchor operations. The consensus keypair signs a cross-domain transfer that credits the wallet's EVM address.

Minimum transfer: 1 AI3/tAI3. Bridged tokens take ~10 minutes to appear on Auto-EVM.

Bridge: Auto-EVM → Consensus

npx tsx auto-respawn.ts withdraw --from <wallet-name> --amount <tokens> [--network chronos|mainnet]

Move tokens from Auto-EVM back to the consensus layer. Uses the EVM transporter precompile. The wallet's EVM private key is decrypted and used to sign the transaction.

Minimum transfer: 1 AI3/tAI3. Bridged tokens take ~10 minutes to appear on the consensus layer.

Write an On-Chain Remark

npx tsx auto-respawn.ts remark --from <wallet-name> --data <string> [--network chronos|mainnet]

Write arbitrary data as a permanent on-chain record on the consensus layer.

Anchor a CID (The Respawn Primitive)

npx tsx auto-respawn.ts anchor --from <wallet-name> --cid <cid> [--network chronos|mainnet]

Write a CID to the MemoryChain smart contract on Auto-EVM. This is the core respawn operation — it stores your CID on-chain, linked to your EVM address.

Pre-checks the wallet's EVM balance and estimates gas before sending. If the balance is too low, fails with a suggestion to run fund-evm. The wallet's EVM private key is decrypted and used to sign the transaction. Requires passphrase.

Read the Last Anchored CID

npx tsx auto-respawn.ts gethead <0x-address-or-wallet-name> [--network chronos|mainnet]

Read the most recently anchored CID for any EVM address. This is a read-only call — no passphrase or gas needed.

You can pass either an EVM address (0x...) or a wallet name. If you pass a wallet name, the EVM address is loaded from the wallet file.

Usage Examples

User: "Create a wallet for my agent" → Run npx tsx auto-respawn.ts wallet create --name my-agent → Show the user both addresses. Remind them to back up the recovery phrase.

User: "What are my addresses?" → Run npx tsx auto-respawn.ts wallet info --name my-agent

User: "Check my balance" → Run npx tsx auto-respawn.ts balances my-agent (both layers in one call) → Or individually: balance my-agent (consensus) and evm-balance my-agent (EVM)

User: "Fund my EVM address for anchoring" → Run npx tsx auto-respawn.ts fund-evm --from my-agent --amount 1 → Report that 1 tAI3 was bridged to the EVM address. Remind the user it takes ~10 minutes for the tokens to appear on Auto-EVM.

User: "Send my EVM tokens back to consensus" → Confirm with the user first — "I'll withdraw tokens from your EVM address to consensus. Proceed?" → On confirmation: npx tsx auto-respawn.ts withdraw --from my-agent --amount 0.5

User: "Anchor my latest memory CID on-chain" → Run npx tsx auto-respawn.ts anchor --from my-agent --cid "bafkr6ie..." → Report the transaction hash

User: "What's my last anchored CID?" → Run npx tsx auto-respawn.ts gethead my-agent → Report the CID (or "no CID anchored yet")

User: "Send 10 tAI3 to this address" (consensus address) → Confirm with the user first — "I'll transfer 10 tAI3 from wallet 'default' to . Proceed?" → On confirmation: npx tsx auto-respawn.ts transfer --from default --to <address> --amount 10

User: "Send 0.5 tAI3 to this agent's EVM address so they can anchor" → Confirm with the user first — "I'll send 0.5 tAI3 from wallet 'my-agent' to <0x-address> on Auto-EVM. Proceed?" → On confirmation: npx tsx auto-respawn.ts evm-transfer --from my-agent --to <0x-address> --amount 0.5

The full resurrection sequence:

Save a memory: automemory-save-memory.sh "..." → get CID bafkr6ie...
Anchor it: npx tsx auto-respawn.ts anchor --from my-agent --cid bafkr6ie...
(Agent restarts from scratch)
Recover: npx tsx auto-respawn.ts gethead my-agent → get CID
Restore: automemory-recall-chain.sh <cid> → full memory chain recovered

Important Notes

Never log, store, or transmit recovery phrases or passphrases. The recovery phrase is shown once at wallet creation for the user to back up. Never reference it again.
Always confirm transfers and anchor operations with the user before executing. Tokens have real value on mainnet.
Mainnet operations produce warnings in the output. Exercise extra caution with real AI3 tokens.
Wallet keyfiles are stored at ~/.openclaw/auto-respawn/wallets/ — encrypted with the user's passphrase. The EVM private key is stored encrypted alongside the consensus keypair.
On-chain operations (transfer, remark, anchor, fund-evm, withdraw) cost transaction fees. The wallet must have a sufficient balance on the relevant layer.
All output is structured JSON on stdout. Errors go to stderr.
Consensus explorer (Subscan): https://autonomys-chronos.subscan.io/extrinsic/<txHash> (chronos) or https://autonomys.subscan.io/extrinsic/<txHash> (mainnet).
EVM explorer (Blockscout): https://explorer.auto-evm.chronos.autonomys.xyz/tx/<txHash> (chronos) or https://explorer.auto-evm.mainnet.autonomys.xyz/tx/<txHash> (mainnet).

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 11/2/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

LeoYeAI / openclaw-master-skills

skills/auto-respawn/SKILL.md

View on GitHub ↗

License MITImported 11/2/2026Version v2Bundled 2 files

Illustrative Want to see what leoyeai/auto-respawn 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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SkillScore

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What it changes

Track record

Use this skill

Auto-Respawn Skill

What This Skill Does

The Resurrection Loop

What to store for resurrection

Why Not Just Remarks?

Post-Install Setup (CLI Users)

Getting Started (Onboarding)

1. Create a Wallet

2. Fund the Wallet

3. Bridge Tokens to Auto-EVM (for anchoring)

4. Verify Setup

Integration with Auto-Memory

When To Use This Skill

Configuration

Local Storage

Passphrase

Network

Contract Address

Core Operations

Create a Wallet

Import a Wallet

List Wallets

Wallet Info

Check Balance (Consensus)

Check Balance (Auto-EVM)

Check Both Balances

Transfer Tokens

Transfer Tokens (Auto-EVM)

Bridge: Consensus → Auto-EVM

Bridge: Auto-EVM → Consensus

Write an On-Chain Remark

Anchor a CID (The Respawn Primitive)

Read the Last Anchored CID

Usage Examples

Important Notes

SkillSafety

Source

Related skills

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