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Deploy Uni Hook · aeonfun | DecimalAI

Codingby aeonfun

Deploy Uni Hook

Generate, simulate, audit, and deploy a Uniswap v4 hook + test pool from a brief, on any Uniswap v4 chain (every testnet and mainnet) - pre-audited templates or a from-scratch freeform hook (flags auto-derived; static audit + dangerous-pattern scan + a behavioral forge test + fork sim gate the deploy). Dry-run by default; explicit arm: to broadcast; testnet default, mainnet behind a double opt-in; records the deploy to main.

Maintained

SkillScore

SkillScore55/100

Test results 55

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%55%of test cases passed +36 pts

Without skill4 of 22

With skill12 of 22

8 fixed4 passed either way10 still failing0 broke

Measured on gemini-3.6-flash8/16/2026v114 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhich template mode is selected automatically for a brief stating 'take a cut of swap revenue for protocol treasury' without setting template:?

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 aeonfun-deploy-uni-hook
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("aeonfun-deploy-uni-hook")
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 aeonfun-deploy-uni-hook

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/aeonfun-deploy-uni-hook/SKILL.md https://app.decimal.ai/s/aeonfun-deploy-uni-hook/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aeonfun-deploy-uni-hook/SKILL.md

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

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

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

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

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

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

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

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/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/aeonfun-deploy-uni-hook/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

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

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

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

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

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

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add aeonfun-deploy-uni-hook

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("aeonfun-deploy-uni-hook")
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/aeonfun-deploy-uni-hook/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add aeonfun-deploy-uni-hook

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/aeonfun-deploy-uni-hook/SKILL.md https://app.decimal.ai/s/aeonfun-deploy-uni-hook/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aeonfun-deploy-uni-hook/SKILL.md

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

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/aeonfun-deploy-uni-hook/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aeonfun-deploy-uni-hook.json

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

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/16/2026
Source: aeonfun/aeon ↗
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

${var} — the hook brief. Grammar: [arm:][template:<name>] [chain:<name>] <brief>

`` (empty) → print help and exit DEPLOY_HOOK_EMPTY.

<brief> → dry-run: generate, compile, mine, and simulate. Never broadcasts. [default — no prefix]

arm:<brief> → broadcast: do the full dry-run first, then deploy for real if the simulation passes.

template:<name> → force a mode: dynamic | noop | skim (pre-audited templates) or freeform (build a whole hook from the prompt). Omit to auto-pick: a brief that matches a template uses it; anything else → freeform.

chain:<name> → any Uniswap v4 chain in chains.tsv (run ./hook-deploy.sh chains to list). Default base-sepolia. Testnets: base-sepolia, unichain-sepolia, arbitrum-sepolia. Mainnets (testnet: false, e.g. base, ethereum, unichain, arbitrum, optimism, polygon, bnb, avalanche, ...) require BOTH arm: and an explicit chain: — the skill never targets mainnet by default. base-mainnet is accepted as an alias for base.

Today is ${today}. This skill turns a one-line brief into a live Uniswap v4 hook. It is built to be safe: it simulates every deploy before it broadcasts, it defaults to a dry-run on testnet, and it needs an explicit arm: to move on-chain.

Why this design

A hook binding is immutable and a bad hook can brick a pool or steal funds. So the gates sit BEFORE the deploy: two of them (dry-run then arm:), a mandatory simulation, and idempotent state. Everything after the broadcast is just recording what already happened — appended to memory/state/hook-deploys.json on main, no PR (there is nothing left to review). The Foundry flow is the proven one — mine a CREATE2 salt so the address carries the right hook-flag bits, deploy, initialize the pool, add liquidity, run one swap.

Safety contract (do not skip)

Mainnet needs a triple lock. Never target a testnet: false chain unless ${var} has BOTH arm: AND an explicit chain:<mainnet-name> — AND the instance has HOOK_MAINNET_OK=1 set (a third, operator-level lock enforced inside hook-deploy.sh, exit 7). An instance that never authorized mainnet cannot broadcast there even if an armed message asks it to. This skill must only run on an instance whose inbound path is owner-gated (TELEGRAM_ALLOWED_USER_ID / the multi-channel allowlist) — a mainnet deploy spends real gas, so an untrusted sender must never be able to dispatch it. On a mainnet chain, first read the deployer balance with cast balance and abort (DEPLOY_HOOK_UNDERFUNDED) if it cannot cover the simulation's Estimated amount required; hook-deploy.sh independently enforces a funding floor (exit 8), an optional MAX_GAS_GWEI gas-price ceiling (exit 9), and warns if the deployer holds more than HOOK_MAX_FLOAT_ETH (default 0.25) — a deploy key must hold gas float only, never LP or treasury capital. Log a clear MAINNET warning in the output.
Simulate before every broadcast. If the simulation reverts, do not broadcast. Report the revert and exit DEPLOY_HOOK_SIM_FAILED.
Dry-run is the default. Broadcast only when ${var} starts with arm:.
Key hygiene. The deployer key is a burner. Never print it. Never put it on a shell command line — always go through ./hook-deploy.sh, which reads it from the env inside the script.
Idempotency. Before broadcasting, read memory/state/hook-deploys.json. If an identical brief already deployed within the last hour, do not re-deploy. The deploy script is also idempotent at the address level: it deploys to the canonical address (the first flag-matching CREATE2 salt for this exact (creationCode, flags, PoolManager)). If that address already holds code, an identical hook is already live, so the script logs ALREADY_DEPLOYED <addr> and does nothing — the runner reports the existing address instead of deploying a duplicate. (HookMiner itself skips occupied addresses, so without this check a re-run would silently deploy another copy at a new address.)

Inputs and config

Templates: skills/deploy-uni-hook/templates/ — DynamicFeeHook.sol, NoOpHook.sol, HookFeeHook.sol (pre-audited), Hook.sol + Hook.t.sol + hook.env.example (freeform scaffold, behavioral-test gate, manifest), plus DeployHook.s.sol, MockERC20.sol, foundry.toml, chains.tsv.
Chain config: skills/deploy-uni-hook/templates/chains.tsv is the single source of truth — TAB-separated name chainId testnet poolManager stateView rpc explorer alchemy, one row per Uniswap v4 chain (staged next to hook-deploy.sh, which reads it). memory/uni-deployments.md mirrors it for humans. To add a chain, append a row to chains.tsv.
Authenticated RPC: the rpc column is a public endpoint. When ALCHEMY_API_KEY is set and the row has an alchemy slug, hook-deploy.sh uses https://<slug>.g.alchemy.com/v2/$ALCHEMY_API_KEY instead — a trusted RPC matters for the mainnet sim + broadcast (a lying public RPC can fake a clean sim). Precedence: RPC_URL (override, for testing) > Alchemy key + slug > public rpc. The RPC path (where the key lives) is never printed — logs show host only.
Deploy helper: skills/deploy-uni-hook/hook-deploy.sh — the only sanctioned broadcast path (hides the key).
State: memory/state/hook-deploys.json — idempotency + the deploy ledger.

Template picker (when `template:` is not given)

Brief mentions	Mode
fee, volatility, dynamic, surge	`dynamic`
skim, hook fee, take a cut, revenue	`skim`
"minimal" / "starter" / "empty"	`noop`
anything else (novel logic the templates don't cover)	`freeform`

Steps

Parse ${var}. Extract the arm: flag, the optional template:, the optional chain:, and the free-text brief. Empty brief → exit DEPLOY_HOOK_EMPTY with the grammar.
Resolve the chain. The chain name resolves in chains.tsv (default base-sepolia); hook-deploy.sh maps it to the official PoolManager + RPC, so you pass the NAME, not the address. Run ./hook-deploy.sh chains to see the list, or read chains.tsv. If the name is not in the registry, exit DEPLOY_HOOK_BAD_CHAIN. Look up the row's testnet column: if it is false (mainnet), enforce the double opt-in — require BOTH arm: and an explicit chain: in ${var}, else exit DEPLOY_HOOK_BAD_CHAIN. Every Uniswap v4 chain is supported (Base, Ethereum, Unichain, Arbitrum, Optimism, Polygon, BNB, Avalanche, Robinhood, Worldchain, Ink, Soneium, Celo, X Layer + their testnets).
Confirm the staged toolchain + project. The workflow pre-stages everything before this run (scripts/stage-deploy-uni-hook.sh): Foundry on $PATH, a pre-built v4 project at $HOOKBUILD_DIR (default $HOME/hookbuild) holding all three templates + MockERC20.sol + DeployHook.s.sol + the v4 libraries, and ./hook-deploy.sh copied to the repo root. Do not install Foundry or clone the libs in-run — the sandbox blocks that. Check command -v forge and that $HOOKBUILD_DIR exists; if either is missing, degrade to DEPLOY_HOOK_NO_TOOLCHAIN (emit the generated source + plan).
Build the hook (brief-driven).
- Template mode (dynamic / noop / skim): in $HOOKBUILD_DIR/src/<Hook>.sol, edit ONLY the region between // --- AEON:LOGIC START --- and // --- AEON:LOGIC END ---. Keep the callback signatures and flag set unchanged. If the default already fits the brief, leave it.
- Freeform mode (anything else): write the whole hook into $HOOKBUILD_DIR/src/Hook.sol — replace the // --- AEON:BODY ... --- region. Rules: keep the contract name Hook and constructor(IPoolManager); implement whichever v4 callbacks the prompt needs, each with the EXACT IHooks signature, onlyPoolManager, and the right selector return. Do NOT hand-set flags — they are auto-derived from which callbacks you implement. If a callback returns a non-zero delta, set HOOK_RETURNS_DELTA in $HOOKBUILD_DIR/hook.env; for a fee-override hook set HOOK_POOL_FEE=dynamic there.
  - Also write the behavioral test. In $HOOKBUILD_DIR/test/Hook.t.sol, replace the // --- AEON:ASSERT ... --- region with test_* functions that assert the hook's SPECIFIC intended behavior — not just "does not revert". For every rule in the brief write at least one positive and one negative case: a swap the hook must REJECT as _expectSwapRevert(zeroForOne, amount, Hook.SomeError.selector) (this helper unwraps v4's WrappedError for you — do NOT use bare vm.expectRevert, it won't match the wrapper); a swap it must ALLOW as a plain _swap(...); any getter/accounting as assertEq(hook.someGetter(...), expected). Do NOT edit setUp() or the helpers — only the AEON:ASSERT region. If the brief has no rejectable behavior, still assert the observable state the hook changes.
Simulate + audit (always). Pass mode, kind, and chain (chain omitted = base-sepolia):
```
./hook-deploy.sh simulate <kind> <chain>
```
For freeform this runs, in order, three gates before any deploy:
1. Static audit — derives the flags from the callbacks; checks the contract is named Hook, has ≥1 callback, every callback carries onlyPoolManager, test/Hook.t.sol has ≥1 test_ function, and scans for dangerous patterns (selfdestruct/delegatecall are hard fails; tx.origin/raw value-call/inline assembly print a warning to review). A failure exits DEPLOY_HOOK_AUDIT_FAILED (never deploy).
2. Behavioral test — forge test --fork-url <chain> --match-contract HookBehaviorTest runs the agent-written assertions on a fork. A failing OR non-compiling test exits DEPLOY_HOOK_TEST_FAILED (never deploy). This proves the hook does what the prompt asked.
3. Fork simulation — forge script compiles, mines the salt, deploys in-memory, initializes the pool, adds liquidity, and runs one swap against a fork of the target chain. On a compile error, fix and retry (max 3). On a sim revert, exit DEPLOY_HOOK_SIM_FAILED. Capture the mined hook address, the derived flags, and the Estimated amount required. On mainnet, compare that estimate to the deployer balance (cast balance <addr> --rpc-url <rpc>) and exit DEPLOY_HOOK_UNDERFUNDED if it will not cover it. For a freeform hook, also read the generated Hook.sol and reason about safety before arming: does any callback let a caller steal funds, brick the pool (unconditional revert), or reenter? If unsure, stop at the dry-run and report the concern.
Dry-run stop. If ${var} did NOT start with arm:, STOP here. Report: template, mined address (with its flag bits), the pool key, and the simulation result. Exit DEPLOY_HOOK_DRY_RUN.
Arm checks (only if arm:).
- Confirm HOOK_DEPLOYER_PRIVATE_KEY is set (it is injected via requires:). If not, degrade to the dry-run report and exit DEPLOY_HOOK_NO_KEY.
- Read memory/state/hook-deploys.json. If the same (chain, template, brief) deployed in the last hour, exit DEPLOY_HOOK_IDEMPOTENT with the prior address.
Broadcast.
```
./hook-deploy.sh broadcast <kind> <chain>
```
The runner prints a deploy receipt (hook address, decoded flag names, explorer deep-link, tx hashes) and, when ETHERSCAN_API_KEY is set on an Etherscan-family chain, auto-verifies the hook source on the explorer (best-effort — a failed verify never fails a completed deploy). If it printed ALREADY_DEPLOYED, treat the reported address as the result (no new deploy). Read the hook address and the transaction hashes from the receipt or $HOOKBUILD_DIR/broadcast/DeployHook.s.sol/<chainId>/run-latest.json.
Verify. With cast, read the pool back through StateView.getSlot0(poolId) on the RPC. Confirm the pool exists and the hook address low bits equal the template's flags. Confirm the swap emitted the hook event.
Record the deploy. The deploy already happened on-chain — this is append-only history, not a change to review, so DO NOT open a PR or a branch. Just write the record into the working tree on main; the workflow's post-run commit lands it. Write:
- memory/state/hook-deploys.json — append this deploy (chain, template, brief, hook address, flags, tx hashes, timestamp, poolId, poolKey).
- output/hooks/<hook-address>.sol — copy the deployed source from $HOOKBUILD_DIR/src/<Hook>.sol.
- For freeform, also output/hooks/<hook-address>.t.sol — copy $HOOKBUILD_DIR/test/Hook.t.sol (the behavioral test that gated the deploy).
Do NOT stage the root ./hook-deploy.sh or ./chains.tsv (runtime copies; both gitignored).
Notify + exit. Send a short notification (template, address, explorer link, dry-run vs live). Exit DEPLOY_HOOK_OK (or DEPLOY_HOOK_DRY_RUN).

Degrade rules

No key → dry-run report, DEPLOY_HOOK_NO_KEY. Never fail hard.
Foundry or the staged project missing (command -v forge fails or $HOOKBUILD_DIR absent) → emit the generated source + plan, DEPLOY_HOOK_NO_TOOLCHAIN. Do not try to install in-run (the sandbox blocks it).
Bad/missing chain, or mainnet without the double opt-in → DEPLOY_HOOK_BAD_CHAIN.
Mainnet chain but the instance did not set HOOK_MAINNET_OK=1 (hook-deploy.sh exit 7) → DEPLOY_HOOK_MAINNET_NOT_AUTHORIZED (never broadcast).
Mainnet balance below the simulation estimate, or the deployer is unfunded (hook-deploy.sh exit 8) → DEPLOY_HOOK_UNDERFUNDED (never broadcast).
Gas price above MAX_GAS_GWEI (hook-deploy.sh exit 9) → DEPLOY_HOOK_GAS_TOO_HIGH (never broadcast; retry when fees drop).
Freeform static audit fails (bad name / no callback / missing onlyPoolManager / no test_ / selfdestruct / delegatecall) → DEPLOY_HOOK_AUDIT_FAILED (never deploy).
Freeform behavioral test fails or does not compile → DEPLOY_HOOK_TEST_FAILED (never deploy).
Simulation revert → DEPLOY_HOOK_SIM_FAILED (never broadcast after a failed sim).

Notes

The three templates are pre-validated: each compiles and simulates a full deploy + swap on Base Sepolia (dynamic = 0x10C0 flags, noop = 0x80, skim = 0x44).
Freeform builds an arbitrary hook from the prompt into src/Hook.sol and its behavioral test into test/Hook.t.sol. Flags are auto-derived from the callbacks (never hand-set). Three gates run before any deploy: a static audit (name/callbacks/onlyPoolManager/test-present/dangerous-pattern scan), the agent-written forge test behavioral assertions on a fork, then the fork simulation. The agent also reads the generated source for steal/brick/reentrancy risk before arming. Prefer a matching template when one fits (they are audited); use freeform for novel logic.
Every deploy — template or freeform — always simulates on the target chain's fork first, so "does it work" is checked before any broadcast.
Any Uniswap v4 chain works. chains.tsv carries every official v4 deployment (Base, Ethereum, Unichain, Arbitrum, Optimism, Polygon, BNB, Avalanche, Robinhood, Worldchain, Ink, Soneium, Celo, X Layer + the Sepolia testnets), each verified to hold the PoolManager. The same flow runs on all of them — only the PoolManager/RPC differ, resolved by name. The CREATE2 deployer (0x4e59…4956C) is required for the mined address; if a chain lacks it the fork simulation fails closed before any broadcast.
Mainnet is gas-only. The deploy mints its own MockERC20 tokens to itself (free) and seeds the demo pool with those mock tokens — a mainnet broadcast risks GAS ONLY, never real capital. The deployed pool is a MockA/MockB demo; the reusable hook contract is the real deliverable. The deployer key must be a funded burner holding gas float only (the runner warns above HOOK_MAX_FLOAT_ETH); mainnet also needs the HOOK_MAINNET_OK=1 operator lock. A future version can add the keyless Base MCP send_calls rail so no key sits in the runner.
Authenticated RPC + receipt + verify. On mainnet the runner prefers an Alchemy endpoint (ALCHEMY_API_KEY + the chain's alchemy slug) over the public RPC, so a lying public node can't fake a clean sim. After a broadcast it prints a receipt (address, decoded flags, explorer link, tx hashes) and, with ETHERSCAN_API_KEY on an Etherscan-family chain, auto-verifies the source (best-effort). All of this is opt-in: with no keys set the skill still runs on public RPCs, unverified.

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aeonfun / aeon

skills/deploy-uni-hook/SKILL.md

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License MITImported 8/10/2026Version v1Bundled 10 files

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