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Hybrid Cloud Outboxes · getsentry | DecimalAI

Codingby getsentry

Hybrid Cloud Outboxes

Sentry uses a **transactional outbox pattern** for eventually consistent operations. When a model changes, an outbox row is written inside the same database transaction. After the transaction commits, the outbox is drained — firing a signal that triggers side effects such as RPC calls, tombstone pro

Never made a case worseMaintained

SkillScore

SkillScore88/100

Test results 88

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

20%88%of test cases passed +68 pts

Without skill5 of 25

With skill22 of 25

17 fixed5 passed either way3 still failing0 broke

Measured on gemini-3.6-flash9/23/2026v223 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskBefore opening a pull request for a new replicated model, I need to check my OutboxCategory setup. What requirement must be verified regarding OutboxScope binding?

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 hybrid-cloud-outboxes
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("hybrid-cloud-outboxes")
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 hybrid-cloud-outboxes

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/hybrid-cloud-outboxes/SKILL.md https://app.decimal.ai/s/hybrid-cloud-outboxes/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/hybrid-cloud-outboxes/SKILL.md

This gets SKILL.md only. The skill's 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/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/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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("hybrid-cloud-outboxes")
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/hybrid-cloud-outboxes/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/hybrid-cloud-outboxes/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 4 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 hybrid-cloud-outboxes

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/hybrid-cloud-outboxes/SKILL.md https://app.decimal.ai/s/hybrid-cloud-outboxes/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/hybrid-cloud-outboxes/SKILL.md

This gets SKILL.md only. The skill's 4 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/hybrid-cloud-outboxes/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/hybrid-cloud-outboxes.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/23/2026
Source: getsentry/sentry ↗
License: NOASSERTION

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

Hybrid Cloud Outboxes

Sentry uses a transactional outbox pattern for eventually consistent operations. When a model changes, an outbox row is written inside the same database transaction. After the transaction commits, the outbox is drained — firing a signal that triggers side effects such as RPC calls, tombstone propagation, or audit logging.

The most common use case is cross-silo data replication: a model saved in the Cell silo produces a CellOutbox that, when processed, replicates data to the Control silo (or vice versa via ControlOutbox). But the pattern is general — outboxes work for any operation that should happen reliably after a transaction commits, even within a single silo.

There are two outbox types corresponding to the two directions of flow:

CellOutbox — written in a Cell silo, processed in the Cell silo to push data toward Control (via RPC calls in signal receivers).
ControlOutbox — written in the Control silo, processed in the Control silo to push data toward one or more Cell silos. Each ControlOutbox row targets a specific cell_name.

Critical Constraints

Outboxes MUST be written in the same transaction as the data change. The mixin classes (ReplicatedCellModel, ReplicatedControlModel) enforce this automatically via prepare_outboxes(). If you write outboxes manually, always use outbox_context(transaction.atomic(...)).

Handlers MUST be idempotent. Outboxes can be retried on failure and are coalesced — the handler may receive only the latest version of a change, or be called multiple times for the same change.

drain_shard() MUST NOT run inside a transaction. It acquires SELECT FOR UPDATE locks and processes messages one at a time. Calling it inside a transaction will deadlock or hold locks for too long.

Only the latest payload survives coalescing. Multiple outbox writes for the same (scope, shard_identifier, category, object_identifier) are coalesced — only the row with the highest ID is processed. Never rely on every intermediate payload being delivered.

Every OutboxCategory must be registered to exactly one OutboxScope. An assertion at import time enforces this. A category registered to zero or multiple scopes causes an import crash.

Bulk operations must use the producing manager. Use MyModel.objects.bulk_create() / bulk_update() / bulk_delete() from CellOutboxProducingManager or ControlOutboxProducingManager. Raw querysets bypass outbox creation.

Snowflake ID models cannot use bulk_create. The producing manager pre-allocates IDs via SELECT nextval(...), which conflicts with snowflake ID generation. Use individual save() calls instead.

Step 1: Determine What You Need

Intent	Go to
Add outbox replication to a new model	Step 2
Add a new `OutboxCategory` (not tied to a replicated model)	Step 3
Write a manual signal receiver (not using model mixins)	Step 4
Migrate an existing model to use outboxes	Step 5, then Step 6
Set up a backfill for existing data	Step 6
Test outbox-based replication	Step 7
Debug stuck or unprocessed outboxes	Step 8

Step 2: Add Outbox Replication to a New Model

2.1 Choose the Mixin

Data lives in...	Replicates toward...	Mixin	Outbox type
Cell silo	Control silo	`ReplicatedCellModel`	`CellOutbox`
Control silo	Cell silo(s)	`ReplicatedControlModel`	`ControlOutbox`

2.2 `ReplicatedCellModel` Template

Use this when a Cell model needs to replicate data to the Control silo.

from sentry.backup.scopes import RelocationScope
from sentry.db.models import (
    FlexibleForeignKey,
    Model,
    cell_silo_model,
    sane_repr,
)
from sentry.db.models.manager.base_query_set import BaseQuerySet
from sentry.hybridcloud.outbox.base import ReplicatedCellModel, CellOutboxProducingManager
from sentry.hybridcloud.outbox.category import OutboxCategory


class MyModelManager(CellOutboxProducingManager["MyModel"]):
    """Manager that ensures bulk operations create outboxes."""
    pass


@cell_silo_model
class MyModel(ReplicatedCellModel):
    __relocation_scope__ = RelocationScope.Organization

    # Required: the OutboxCategory for this model (must already be registered)
    category = OutboxCategory.MY_MODEL_UPDATE

    # Use the producing manager for bulk operation support
    objects: ClassVar[MyModelManager] = MyModelManager()

    # Model fields...
    organization = FlexibleForeignKey("sentry.Organization")
    name = models.CharField(max_length=128)

    class Meta:
        app_label = "sentry"
        db_table = "sentry_mymodel"

    def payload_for_update(self) -> dict[str, Any] | None:
        """
        Optional: include data needed by the deletion handler.
        Keep payloads minimal — only data that cannot be recovered
        after the row is deleted. Payloads are coalesced (only the
        latest survives).
        """
        return None  # Override if needed

    @classmethod
    def handle_async_deletion(
        cls,
        identifier: int,
        shard_identifier: int,
        payload: Mapping[str, Any] | None,
    ) -> None:
        """
        Called when this object has been deleted (row no longer exists).
        Clean up cross-silo resources. Must be idempotent.
        """
        my_mapping_service.delete(
            my_model_id=identifier,
            organization_id=shard_identifier,
        )

    def handle_async_replication(self, shard_identifier: int) -> None:
        """
        Called when this object has been created or updated.
        Replicate to the control silo via RPC. Must be idempotent.
        """
        my_mapping_service.upsert(
            my_model_id=self.id,
            organization_id=shard_identifier,
            mapping=RpcMyModelMapping.from_orm(self),
        )

2.3 `ReplicatedControlModel` Template

Use this when a Control model needs to replicate data to Cell silo(s). The key difference: Control outboxes fan out to one or more cells, so the model must declare which cells to target.

from sentry.db.models import control_silo_model
from sentry.hybridcloud.outbox.base import ReplicatedControlModel, ControlOutboxProducingManager
from sentry.hybridcloud.outbox.category import OutboxCategory


class MyControlModelManager(ControlOutboxProducingManager["MyControlModel"]):
    pass


@control_silo_model
class MyControlModel(ReplicatedControlModel):
    __relocation_scope__ = RelocationScope.Global

    category = OutboxCategory.MY_CONTROL_MODEL_UPDATE

    objects: ClassVar[MyControlModelManager] = MyControlModelManager()

    # Model fields...
    organization = FlexibleForeignKey("sentry.Organization")
    user = FlexibleForeignKey("sentry.User")

    class Meta:
        app_label = "sentry"
        db_table = "sentry_mycontrolmodel"

    def outbox_cell_names(self) -> Collection[str]:
        """
        Which cells should receive outboxes for this change.
        Default implementation checks organization_id then user_id.
        Override for custom logic (e.g., all cells, specific cells).
        """
        # Default: auto-detects from organization_id or user_id attributes.
        # Override only if the default doesn't work for your model.
        return super().outbox_cell_names()

    @classmethod
    def handle_async_deletion(
        cls,
        identifier: int,
        cell_name: str,
        shard_identifier: int,
        payload: Mapping[str, Any] | None,
    ) -> None:
        """Note: receives cell_name — one call per target cell."""
        pass

    def handle_async_replication(self, cell_name: str, shard_identifier: int) -> None:
        """Note: receives cell_name — one call per target cell."""
        pass

2.4 Wire Up the Category Connection

The mixin classes auto-connect signal receivers via OutboxCategory.connect_cell_model_updates() (or connect_control_model_updates()). This happens at class definition time when the category class variable is set. The connection dispatches to your handle_async_replication and handle_async_deletion methods automatically.

No manual signal receiver is needed for replicated models — the mixin handles it. Manual receivers are only needed for categories that don't map to a replicated model (see Step 4).

If your OutboxCategory doesn't exist yet, create it first (Step 3).

Step 3: Add a New OutboxCategory

Every outbox message type needs an OutboxCategory enum value registered to exactly one OutboxScope.

Quick steps:

Add a new value to the OutboxCategory enum in src/sentry/hybridcloud/outbox/category.py
Register it under the appropriate OutboxScope (determines the shard key)
If using model mixins, set category = OutboxCategory.MY_CATEGORY on the model

Load references/category-and-scope.md for the full scope-to-category mapping, how to pick a scope, and registration mechanics.

Step 4: Write a Manual Signal Receiver

Use manual receivers when the outbox category is not tied to a ReplicatedCellModel or ReplicatedControlModel. Common cases:

Payload-only operations (audit logs, IP events) that carry all data in the payload
Actions triggered by a model change but not replicating that model directly
Cross-silo signal forwarding (SEND_SIGNAL, RESET_IDP_FLAGS)
Complex multi-step operations requiring custom dispatch logic

Load references/signal-receivers.md for copy-paste receiver templates, the maybe_process_tombstone pattern, and placement rules.

Step 5: Migrate an Existing Model to Use Outboxes

When adding outbox replication to a model that already has data in production:

5.1 Code Changes (Non-Breaking)

Change the model's base class to ReplicatedCellModel or ReplicatedControlModel
Add the category class variable
Add a producing manager (CellOutboxProducingManager / ControlOutboxProducingManager)
Implement handle_async_replication and handle_async_deletion
If needed, add payload_for_update() for deletion recovery data
Create the OutboxCategory if it doesn't exist (Step 3)

These changes are non-breaking: new model saves will create outboxes, but existing rows have no outboxes yet.

5.2 Backfill Existing Data

Existing rows need outboxes created retroactively. Set replication_version = 2 (or higher) on the model class and configure the backfill system — see Step 6.

Step 6: Set Up a Backfill

The backfill system creates outboxes for existing model rows that predate the outbox integration. It processes rows in batches, tracked by a watermark row in Postgres.

Load references/backfill.md for the replication_version mechanism, option key format, watermark state tracking, and SaaS vs self-hosted rollout procedures.

Step 7: Test Outbox-Based Replication

For detailed outbox test templates and copy-paste patterns, invoke the hybrid-cloud-test-gen skill. The guidance below covers what to test; hybrid-cloud-test-gen covers how to generate the test code.

7.1 Core Test Utilities

outbox_runner() — the primary test tool. Context manager that drains all pending outboxes synchronously after the wrapped code succeeds:

from sentry.testutils.outbox import outbox_runner

with outbox_runner():
    my_model.save()
# All outboxes drained — cross-silo effects have happened

It runs up to 10 drain iterations (raises OutboxRecursionLimitError if exceeded). Works with TestCase — no TransactionTestCase needed for standard outbox tests.

outbox_context(flush=False) — creates outbox records without processing them. Use to verify outbox creation independently of processing:

from sentry.hybridcloud.models.outbox import outbox_context

with outbox_context(flush=False):
    MyModel(id=10).outbox_for_update().save()

assert CellOutbox.objects.count() == 1

assume_test_silo_mode / assume_test_silo_mode_of — switch silo context within a test to query cross-silo models:

from sentry.testutils.silo import assume_test_silo_mode_of

with assume_test_silo_mode_of(MyMapping):
    assert MyMapping.objects.filter(my_model_id=obj.id).exists()

7.2 What to Test

Outbox creation — verify saving/deleting the model creates outbox rows with correct scope, category, and identifiers:

def test_outbox_created_on_save(self):
    with outbox_context(flush=False):
        obj = MyModel(id=10, organization_id=1)
        obj.outbox_for_update().save()

    outbox = CellOutbox.objects.first()
    assert outbox.category == OutboxCategory.MY_MODEL_UPDATE.value
    assert outbox.shard_scope == OutboxScope.ORGANIZATION_SCOPE.value
    assert outbox.shard_identifier == 1

Replication propagates — verify the full round-trip: save model -> drain outboxes -> cross-silo effect:

def test_replication_creates_mapping(self):
    org = self.create_organization()
    with outbox_runner():
        obj = MyModel.objects.create(organization=org, name="test")

    with assume_test_silo_mode_of(MyMapping):
        mapping = MyMapping.objects.get(my_model_id=obj.id)
        assert mapping.name == "test"

Deletion and tombstone — verify deleting the model triggers handle_async_deletion and cleans up cross-silo resources:

def test_delete_cleans_up_mapping(self):
    org = self.create_organization()
    with outbox_runner():
        obj = MyModel.objects.create(organization=org, name="test")

    with outbox_runner():
        obj.delete()

    with assume_test_silo_mode_of(MyMapping):
        assert not MyMapping.objects.filter(my_model_id=obj.id).exists()

Idempotency — verify draining the same shard twice produces no duplicates or errors:

def test_idempotent_replication(self):
    with outbox_runner():
        obj = MyModel.objects.create(organization=org, name="test")

    with assume_test_silo_mode_of(MyMapping):
        count_after_first = MyMapping.objects.count()

    with outbox_runner():
        pass  # Drain again — should be a no-op

    with assume_test_silo_mode_of(MyMapping):
        assert MyMapping.objects.count() == count_after_first

7.3 Silo Test Decorators

Use @cell_silo_test for tests focused on CellOutbox creation
Use @control_silo_test for tests focused on ControlOutbox creation
Use @all_silo_test for end-to-end replication tests that exercise both silos
Only use TransactionTestCase for threading/concurrency tests (e.g., threading.Barrier), not for standard outbox drain tests

7.4 Common Pitfalls

Factory calls (self.create_organization(), etc.) must NEVER be wrapped in assume_test_silo_mode. Factories handle silo mode internally.
outbox_runner() clears outboxes on exit. If you need to inspect outbox state, use outbox_context(flush=False) instead.
If an outbox handler creates more outboxes (cascading), outbox_runner handles this automatically (up to 10 iterations).

Step 8: Debug Stuck Outboxes

Symptom	Likely cause	Investigation
Data not replicating to other silo	Handler error, outbox in backoff	Check `scheduled_for` on stuck outboxes
`OutboxFlushError` in tests	Signal receiver raises an exception	Read the wrapped exception in the error message
Outbox rows accumulating	Drain task not running or failing	Check Celery task logs for `enqueue_outbox_jobs`
Shard draining slowly	Large coalesced batch or handler timeout	Check `outbox.coalesced_net_processing_time` metric
Import crash: scope/category assertion	Category registered to wrong or multiple scopes	Check `OutboxScope` registration in `category.py`

Load references/debugging.md for the full processing pipeline walkthrough, shard inspection methods, backoff schedule, kill switches, and useful SQL/metrics queries.

Step 9: Verify (Pre-flight Checklist)

Before submitting your PR, verify:

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.agents/skills/hybrid-cloud-outboxes/SKILL.md

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License NOASSERTIONImported 9/21/2026Version v2Bundled 4 files

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Hybrid Cloud Outboxes

Critical Constraints

Step 1: Determine What You Need

Step 2: Add Outbox Replication to a New Model

2.1 Choose the Mixin

2.2 ReplicatedCellModel Template

2.3 ReplicatedControlModel Template

2.4 Wire Up the Category Connection

Step 3: Add a New OutboxCategory

Step 4: Write a Manual Signal Receiver

Step 5: Migrate an Existing Model to Use Outboxes

5.1 Code Changes (Non-Breaking)

5.2 Backfill Existing Data

Step 6: Set Up a Backfill

Step 7: Test Outbox-Based Replication

7.1 Core Test Utilities

7.2 What to Test

7.3 Silo Test Decorators

7.4 Common Pitfalls

Step 8: Debug Stuck Outboxes

Step 9: Verify (Pre-flight Checklist)

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2.2 `ReplicatedCellModel` Template

2.3 `ReplicatedControlModel` Template