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Author new samples for the ADK Python repository. Use this skill when the user wants to create a new sample demonstrating a feature or agent pattern (e.g., dynamic nodes, standalone agents, fan-out/fan-in) or when adding examples to subdirectories under `contributing/`.
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This prompt takes no variables — just pick a model and run.
# ADK Sample Creator
This skill helps you create new samples for the ADK Python repository. You should search for subdirectories under `contributing` (such as `new_workflow_samples`, `workflow_samples`, etc.) and confirm with the user which folder they want to use before creating the sample.
> [!TIP]
> Before creating samples, you can use the `adk-style` skill to learn about ADK 2.0 architecture knowledge and best practices.
A sample consists of:
1. A directory per sample.
2. An `agent.py` file defining the agent or workflow logic.
3. A `README.md` file explaining the sample.
## Guidelines
### 1. Folder Name
Use snake_case for the folder name (e.g., `dynamic_nodes`, `fan_out_fan_in`).
### 2. `agent.py` Content
The `agent.py` should focus on demonstrating a specific feature or agent pattern. Use absolute imports for testing convenience.
> [!IMPORTANT]
> **Model Selection**: Do not set the `model` parameter explicitly (e.g., `model="gemini-2.5-flash"`) on `Agent` instances in sample agents. Instead, let them default to the system-configured model, unless a specific model is explicitly requested by the user.
Choose one of the following patterns:
#### Pattern A: Workflows (for complex graphs)
Use this when you need multiple nodes, routing, or parallel execution.
**Imports:**
```python
from google.adk import Agent
from google.adk import Context
from google.adk.workflow import node
from google.adk.workflow import JoinNode
from google.adk.workflow._workflow_class import Workflow
```
**Anatomy:**
```python
my_agent = Agent(name="my_agent", ...)
@node()
async def my_node(node_input: str):
return "result"
root_agent = Workflow(
name="root_wf",
edges=[("START", my_node)],
)
```
#### Pattern B: Standalone Agents (for single-agent or simple tool use)
Use this when you don't need a graph and the agent handles the loop.
**Imports:**
```python
from google.adk import Agent
from google.adk.tools import google_search # example
```
**Anatomy:**
```python
root_agent = Agent(
name="standalone_assistant",
instruction="You are a helpful assistant.",
description="An assistant that can help with queries.",
tools=[google_search],
)
```
### 3. `README.md` Content
Each sample should have a `README.md` with the following structure:
- **Overview**: What the sample does.
- **Sample Inputs**: Examples of inputs to test with. Each prompt must be wrapped in backticks. If a prompt has an explanation, always add a blank line between the prompt and the explanation, and indent the explanation by two spaces.
- **Graph**: Visualization of the graph flow (Mermaid recommended for workflows).
- **How To**: Explanation of key techniques used (e.g., `ctx.run_node`).
#### README Example Template:
````markdown
# ADK Sample Name
## Overview
Brief description.
## Sample Inputs
- `Prompt example 1`
- `Prompt example 2`
*Explanation or expected behavior*
## Graph
```mermaid
graph TD
START --> MyNode
```
````
## How To
Explain the details.
````
## Examples
### Dynamic Nodes
Snippet from `dynamic_nodes/agent.py`:
```python
@node(rerun_on_resume=True)
async def orchestrate(ctx: Context, node_input: str) -> str:
while True:
headline = await ctx.run_node(generate_headline)
# ...
````
### Fan Out Fan In
Snippet from `fan_out_fan_in/agent.py`:
```python
root_agent = Workflow(
name="root_agent",
edges=[("START", (node_a, node_b), join_node, aggregate)],
)
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Author new samples for the ADK Python repository. Use this skill when the user wants to create a new sample demonstrating a feature or agent pattern (e.g., dynamic nodes, standalone agents, fan-out/fan-in) or when adding examples to subdirectories under `contributing/`.
# ADK Sample Creator
This skill helps you create new samples for the ADK Python repository. You should search for subdirectories under `contributing` (such as `new_workflow_samples`, `workflow_samples`, etc.) and confirm with the user which folder they want to use before creating the sample.
> [!TIP]
> Before creating samples, you can use the `adk-style` skill to learn about ADK 2.0 architecture knowledge and best practices.
A sample consists of:
1. A directory per sample.
2. An `agent.py` file defining the agent or workflow logic.
3. A `README.md` file explaining the sample.
## Guidelines
### 1. Folder Name
Use snake_case for the folder name (e.g., `dynamic_nodes`, `fan_out_fan_in`).
### 2. `agent.py` Content
The `agent.py` should focus on demonstrating a specific feature or agent pattern. Use absolute imports for testing convenience.
> [!IMPORTANT]
> **Model Selection**: Do not set the `model` parameter explicitly (e.g., `model="gemini-2.5-flash"`) on `Agent` instances in sample agents. Instead, let them default to the system-configured model, unless a specific model is explicitly requested by the user.
Choose one of the following patterns:
#### Pattern A: Workflows (for complex graphs)
Use this when you need multiple nodes, routing, or parallel execution.
**Imports:**
```python
from google.adk import Agent
from google.adk import Context
from google.adk.workflow import node
from google.adk.workflow import JoinNode
from google.adk.workflow._workflow_class import Workflow
```
**Anatomy:**
```python
my_agent = Agent(name="my_agent", ...)
@node()
async def my_node(node_input: str):
return "result"
root_agent = Workflow(
name="root_wf",
edges=[("START", my_node)],
)
```
#### Pattern B: Standalone Agents (for single-agent or simple tool use)
Use this when you don't need a graph and the agent handles the loop.
**Imports:**
```python
from google.adk import Agent
from google.adk.tools import google_search # example
```
**Anatomy:**
```python
root_agent = Agent(
name="standalone_assistant",
instruction="You are a helpful assistant.",
description="An assistant that can help with queries.",
tools=[google_search],
)
```
### 3. `README.md` Content
Each sample should have a `README.md` with the following structure:
- **Overview**: What the sample does.
- **Sample Inputs**: Examples of inputs to test with. Each prompt must be wrapped in backticks. If a prompt has an explanation, always add a blank line between the prompt and the explanation, and indent the explanation by two spaces.
- **Graph**: Visualization of the graph flow (Mermaid recommended for workflows).
- **How To**: Explanation of key techniques used (e.g., `ctx.run_node`).
#### README Example Template:
````markdown
# ADK Sample Name
## Overview
Brief description.
## Sample Inputs
- `Prompt example 1`
- `Prompt example 2`
*Explanation or expected behavior*
## Graph
```mermaid
graph TD
START --> MyNode
```
````
## How To
Explain the details.
````
## Examples
### Dynamic Nodes
Snippet from `dynamic_nodes/agent.py`:
```python
@node(rerun_on_resume=True)
async def orchestrate(ctx: Context, node_input: str) -> str:
while True:
headline = await ctx.run_node(generate_headline)
# ...
````
### Fan Out Fan In
Snippet from `fan_out_fan_in/agent.py`:
```python
root_agent = Workflow(
name="root_agent",
edges=[("START", (node_a, node_b), join_node, aggregate)],
)
```