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Hybrid Cloud RPC Services · getsentry | DecimalAI

Codingby getsentry

Hybrid Cloud RPC Services

Guide for creating, updating, and deprecating hybrid cloud RPC services in Sentry.

Never made a case worse

SkillScore

SkillScore59/100

Test results 59

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%59%of test cases passed +45 pts

Without skill3 of 22

With skill13 of 22

10 fixed3 passed either way9 still failing0 broke

Measured on gemini-3.6-flash7/27/2026v121 comparable

Track record

Agent runs: 0; All time

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-rpc
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-rpc")
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-rpc

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

Copy SKILL.md

Save it in your project, as

.claude/skills/hybrid-cloud-rpc/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-rpc

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-rpc")
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-rpc/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-rpc/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-rpc

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

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-rpc")
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-rpc/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-rpc/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-rpc

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

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

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-rpc")
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-rpc/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-rpc

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-rpc")
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-rpc/SKILL.md

Give it to your agent

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

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

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

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-rpc")
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-rpc/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

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

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

Copy SKILL.md

Save it in your project, as

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

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/hybrid-cloud-rpc.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: v1, tested 7/27/2026
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 RPC Services

This skill guides you through creating, modifying, and deprecating RPC services in Sentry's hybrid cloud architecture. RPC services enable cross-silo communication between the Control silo (user auth, org management) and Cell silos (project data, events, issues, billing).

Critical Constraints

NEVER use from __future__ import annotations in service.py or model.py files. The RPC framework reflects on type annotations at import time. Forward references break serialization silently.

ALL RPC method parameters must be keyword-only (use * in the signature).

ALL parameters and return types must have full type annotations — no string forward references.

ONLY serializable types are allowed: int, str, bool, float, None, Optional[T], list[T], dict[str, T], RpcModel subclasses, Enum subclasses, datetime.datetime.

The service MUST live in one of the 12 registered discovery packages (see Step 3).

Use Field(repr=False) on sensitive fields (tokens, secrets, keys, config blobs, metadata dicts) to prevent them from leaking into logs and error reports. See references/rpc-models.md for the full guide.

Step 1: Determine Operation

Classify what the developer needs:

Intent	Go to
Create a brand-new RPC service	Step 2, then Step 3
Add a method to an existing service	Step 2, then Step 4
Update an existing method's signature	Step 5
Deprecate or remove a method/service	Step 6

Step 2: Determine Silo Mode

The service's local_mode determines where the database-backed implementation runs:

Data lives in...	`local_mode`	Decorator on methods	Example
Cell silo (projects, events, issues, org data, billing)	`SiloMode.CELL`	`@cell_rpc_method(resolve=...)`	`OrganizationService`
Control silo (users, auth, org mappings)	`SiloMode.CONTROL`	`@rpc_method`	`OrganizationMemberMappingService`

Decision rule: If the Django models you need to query live in the cell database, use SiloMode.CELL. If they live in the control database, use SiloMode.CONTROL.

Cell-silo services require a CellResolutionStrategy on every RPC method so the framework knows which cell to route remote calls to. Load references/resolvers.md for the full resolver table.

Step 3: Create a New Service

Load references/service-template.md for copy-paste file templates.

Directory structure

src/sentry/{domain}/services/{service_name}/
├── __init__.py      # Re-exports model and service
├── model.py         # RpcModel subclasses (NO future annotations)
├── serial.py        # ORM → RpcModel conversion functions
├── service.py       # Abstract service class (NO future annotations)
└── impl.py          # DatabaseBacked implementation

Registration

The service package MUST be a sub-package of one of these 12 registered discovery packages:

sentry.auth.services
sentry.audit_log.services
sentry.backup.services
sentry.hybridcloud.services
sentry.identity.services
sentry.integrations.services
sentry.issues.services
sentry.notifications.services
sentry.organizations.services
sentry.projects.services
sentry.sentry_apps.services
sentry.users.services

If your service doesn't fit any of these, add a new entry to the service_packages tuple in src/sentry/hybridcloud/rpc/service.py:list_all_service_method_signatures().

Checklist for new services

key is unique across all services (check existing keys with grep -r 'key = "' src/sentry/*/services/*/service.py)
local_mode matches where the data lives
get_local_implementation() returns the DatabaseBacked subclass
Module-level my_service = MyService.create_delegation() at bottom of service.py
__init__.py re-exports models and service
No from __future__ import annotations in service.py or model.py

Step 4: Add or Update Methods

For CELL silo services

Load references/resolvers.md for resolver details.

@cell_rpc_method(resolve=ByOrganizationId())
@abstractmethod
def my_method(
    self,
    *,
    organization_id: int,
    name: str,
    options: RpcMyOptions | None = None,
) -> RpcMyResult | None:
    pass

Key rules:

@cell_rpc_method MUST come before @abstractmethod
The resolver parameter (e.g., organization_id) MUST be in the method signature
Use return_none_if_mapping_not_found=True when the return type is Optional and a missing org mapping means "not found" rather than an error

For CONTROL silo services

@rpc_method
@abstractmethod
def my_method(
    self,
    *,
    user_id: int,
    data: RpcMyData,
) -> RpcMyResult:
    pass

Non-abstract convenience methods

You can also add non-abstract methods that compose other RPC calls. These run locally and are NOT exposed as RPC endpoints:

def get_by_slug_or_id(self, *, slug: str | None = None, id: int | None = None) -> RpcThing | None:
    if slug:
        return self.get_by_slug(slug=slug)
    if id:
        return self.get_by_id(id=id)
    return None

Implementation in impl.py

The DatabaseBacked subclass must implement every @abstractmethod with the exact same parameter names:

class DatabaseBackedMyService(MyService):
    def my_method(self, *, organization_id: int, name: str, options: RpcMyOptions | None = None) -> RpcMyResult | None:
        # ORM queries here
        obj = MyModel.objects.filter(organization_id=organization_id, name=name).first()
        if obj is None:
            return None
        return serialize_my_model(obj)

Error propagation

All errors an RPC method propagates must be done via the return type. Errors are rewrapped and returned as generic Invalid service request to external callers.

class RpcTentativeResult(RpcModel):
    success: bool
    error_str: str | None
    result: str | None

class DatabaseBackedMyService(MyService):
    def foobar(self, *, organization_id: int) -> RpcTentativeResult
        try:
            some_function_call()
        except e:
            return RpcTentativeResult(success=False, error_str = str(e))

        return RpcTentativeResult(success=True, result="foobar")

RPC Models

Load references/rpc-models.md for supported types, default values, and serialization patterns.

Step 5: Update Method Signatures

Safe changes (backwards compatible)

Adding a new optional parameter with a default value
Widening a return type (e.g., RpcFoo → RpcFoo | None) on a Control RPC service
Adding fields with defaults to an RpcModel

Breaking changes (require coordination)

Removing or renaming a parameter
Changing a parameter's type
Narrowing a return type
Removing fields from an RpcModel

For breaking changes, use a two-phase approach:

Add the new method alongside the old one
Migrate all callers to the new method
Remove the old method (see Step 6)

Step 6: Deprecate or Remove

Load references/deprecation.md for the full 3-phase workflow.

Quick summary: Disable at runtime → migrate callers → remove code.

Step 7: Test

Every RPC service needs three categories of tests: silo mode compatibility, data accuracy, and error handling. Use TransactionTestCase (not TestCase) when tests need outbox processing or on_commit hooks.

7.1 Silo mode compatibility with `@all_silo_test`

Every service test class MUST use @all_silo_test so tests run in all three modes (MONOLITH, CELL, CONTROL). This ensures the delegation layer works for both local and remote dispatch paths.

from sentry.testutils.cases import TestCase, TransactionTestCase
from sentry.testutils.silo import all_silo_test, assume_test_silo_mode, create_test_cells

@all_silo_test
class MyServiceTest(TestCase):
    def test_get_by_id(self):
        org = self.create_organization()
        result = my_service.get_by_id(organization_id=org.id, id=thing.id)
        assert result is not None

For tests that need named cells (e.g., testing cell resolution):

@all_silo_test(cells=create_test_cells("us", "eu"))
class MyServiceCellTest(TransactionTestCase):
    ...

Use assume_test_silo_mode or assume_test_silo_mode_of to switch modes within a test when accessing ORM models that live in a different silo:

def test_cross_silo_behavior(self):
    with assume_test_silo_mode(SiloMode.CELL):
        org = self.create_organization()
    result = my_service.get_by_id(organization_id=org.id, id=thing.id)
    assert result is not None

7.2 Serialization round-trip with `dispatch_to_local_service`

Test that arguments and return values survive serialization/deserialization:

from sentry.hybridcloud.rpc.service import dispatch_to_local_service

def test_serialization_round_trip(self):
    result = dispatch_to_local_service(
        "my_service_key",
        "my_method",
        {"organization_id": org.id, "name": "test"},
    )
    assert result["value"] is not None

7.3 RPC model data accuracy

Validate that RPC models faithfully represent the ORM data. Compare every field of the RPC model against the source ORM object:

def test_rpc_model_accuracy(self):
    orm_obj = MyModel.objects.get(id=thing.id)
    rpc_obj = my_service.get_by_id(organization_id=org.id, id=thing.id)

    assert rpc_obj.id == orm_obj.id
    assert rpc_obj.name == orm_obj.name
    assert rpc_obj.organization_id == orm_obj.organization_id
    assert rpc_obj.is_active == orm_obj.is_active
    assert rpc_obj.date_added == orm_obj.date_added

For models with flags or nested objects, iterate all field names:

def test_flags_accuracy(self):
    rpc_org = organization_service.get(id=org.id)
    for field_name in rpc_org.flags.get_field_names():
        assert getattr(rpc_org.flags, field_name) == getattr(orm_org.flags, field_name)

For list results, sort both sides by ID before comparing:

def test_list_accuracy(self):
    rpc_items = my_service.list_things(organization_id=org.id)
    orm_items = list(MyModel.objects.filter(organization_id=org.id).order_by("id"))
    assert len(rpc_items) == len(orm_items)
    for rpc_item, orm_item in zip(sorted(rpc_items, key=lambda x: x.id), orm_items):
        assert rpc_item.id == orm_item.id
        assert rpc_item.name == orm_item.name

7.4 Cross-silo resource creation

If your service creates or updates resources that propagate across silos (via outboxes or mappings), verify the cross-silo effects.

Use outbox_runner() to flush outboxes synchronously during tests:

from sentry.testutils.outbox import outbox_runner

def test_cross_silo_mapping_created(self):
    with outbox_runner():
        my_service.create_thing(organization_id=org.id, name="test")

    with assume_test_silo_mode(SiloMode.CONTROL):
        mapping = MyMapping.objects.get(organization_id=org.id)
        assert mapping.name == "test"

For triple-equality assertions (RPC result = source ORM = cross-silo replica):

def test_provisioning_accuracy(self):
    rpc_result = my_service.provision(organization_id=org.id, slug="test")
    with assume_test_silo_mode(SiloMode.CELL):
        orm_obj = MyModel.objects.get(id=rpc_result.id)
    with assume_test_silo_mode(SiloMode.CONTROL):
        mapping = MyMapping.objects.get(organization_id=org.id)
    assert rpc_result.slug == orm_obj.slug == mapping.slug

Use HybridCloudTestMixin for common cross-silo assertions:

from sentry.testutils.hybrid_cloud import HybridCloudTestMixin

class MyServiceTest(HybridCloudTestMixin, TransactionTestCase):
    def test_member_mapping_synced(self):
        self.assert_org_member_mapping(org_member=org_member)

7.5 Error handling

Test that the service handles errors correctly in all silo modes:

def test_not_found_returns_none(self):
    result = my_service.get_by_id(organization_id=org.id, id=99999)
    assert result is None

def test_missing_org_returns_none(self):
    # For methods with return_none_if_mapping_not_found=True
    result = my_service.get_by_id(organization_id=99999, id=1)
    assert result is None

Test disabled methods:

from sentry.hybridcloud.rpc.service import RpcDisabledException
from sentry.testutils.helpers.options import override_options

def test_disabled_method_raises(self):
    with override_options({"hybrid_cloud.rpc.disabled-service-methods": ["MyService.my_method"]}):
        with pytest.raises(RpcDisabledException):
            dispatch_remote_call(None, "my_service_key", "my_method", {"id": 1})

Test that remote exceptions are properly wrapped:

from sentry.hybridcloud.rpc.service import RpcRemoteException

def test_remote_error_wrapping(self):
    if SiloMode.get_current_mode() == SiloMode.CELL:
        with pytest.raises(RpcRemoteException):
            my_control_service.do_thing_that_fails(...)

Test that failed operations produce no side effects:

def test_no_side_effects_on_failure(self):
    result = my_service.create_conflicting_thing(organization_id=org.id)
    assert not result
    with assume_test_silo_mode(SiloMode.CELL):
        assert not MyModel.objects.filter(organization_id=org.id).exists()

Test that any calling code (both direct and indirect) is also appropriately tested with the correct silo decorators.

7.6 Key imports for testing

from sentry.testutils.cases import TestCase, TransactionTestCase
from sentry.testutils.silo import (
    all_silo_test,
    control_silo_test,
    cell_silo_test,
    assume_test_silo_mode,
    assume_test_silo_mode_of,
    create_test_cells,
)
from sentry.testutils.outbox import outbox_runner
from sentry.testutils.hybrid_cloud import HybridCloudTestMixin
from sentry.hybridcloud.rpc.service import (
    dispatch_to_local_service,
    dispatch_remote_call,
    RpcDisabledException,
    RpcRemoteException,
)

Step 8: Verify (Pre-flight Checklist)

Before submitting your PR, verify:

When it activates

Automatically, when a task matches. Use when asked to "add RPC method", "create RPC service", "hybrid cloud service", "new RPC model", "deprecate RPC method", "remove RPC endpoint", "cross-silo service", "cell RPC", or "control silo service". Covers service scaffolding, method signatures, RPC models, cell resolvers, testing, and safe deprecation workflows.

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

getsentry / sentry

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

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License NOASSERTIONImported 7/31/2026Version v1Bundled 4 files

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

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What it changes

Track record

Use this skill

Hybrid Cloud RPC Services

Critical Constraints

Step 1: Determine Operation

Step 2: Determine Silo Mode

Step 3: Create a New Service

Directory structure

Registration

Checklist for new services

Step 4: Add or Update Methods

For CELL silo services

For CONTROL silo services

Non-abstract convenience methods

Implementation in impl.py

Error propagation

RPC Models

Step 5: Update Method Signatures

Safe changes (backwards compatible)

Breaking changes (require coordination)

Step 6: Deprecate or Remove

Step 7: Test

7.1 Silo mode compatibility with @all_silo_test

7.2 Serialization round-trip with dispatch_to_local_service

7.3 RPC model data accuracy

7.4 Cross-silo resource creation

7.5 Error handling

7.6 Key imports for testing

Step 8: Verify (Pre-flight Checklist)

When it activates

SkillSafety

Source

Related skills

More in the registry

7.1 Silo mode compatibility with `@all_silo_test`

7.2 Serialization round-trip with `dispatch_to_local_service`