Agent skill
flowerpower
Create and manage data pipelines using the FlowerPower framework with Hamilton DAGs and uv. Use when users request creating flowerpower projects, pipelines, Hamilton dataflows, or ask about flowerpower configuration, execution, or CLI commands.
Install this agent skill to your Project
npx add-skill https://github.com/legout/flowerpower-skill/tree/main/flowerpower
SKILL.md
FlowerPower Pipeline Skill
Create and manage data processing pipelines using FlowerPower with Hamilton DAGs.
Quick Start
# Install flowerpower
uv pip install flowerpower
# Initialize project
flowerpower init --name my-project
# Create pipeline
flowerpower pipeline new my_pipeline
# Run pipeline
flowerpower pipeline run my_pipeline
Project Initialization
Use scripts/init_project.py or CLI:
# CLI
flowerpower init --name <project-name>
# Python
from flowerpower import FlowerPowerProject
project = FlowerPowerProject.init(name='my-project')
Creates structure:
my-project/
├── conf/
│ ├── project.yml
│ └── pipelines/
├── pipelines/
└── hooks/
Creating Pipelines
Use scripts/create_pipeline.py or CLI:
flowerpower pipeline new <name>
Creates:
pipelines/<name>.py- Hamilton functionsconf/pipelines/<name>.yml- Configuration
Pipeline Module Template
from hamilton.function_modifiers import parameterize
from pathlib import Path
from flowerpower.cfg import Config
PARAMS = Config.load(
Path(__file__).parents[1], pipeline_name="my_pipeline"
).pipeline.h_params
@parameterize(**PARAMS.input_config)
def load_data(source: str) -> dict:
"""Load data from source."""
return {"source": source}
def process_data(load_data: dict) -> dict:
"""Process loaded data."""
return {"processed": load_data}
def final_result(process_data: dict) -> str:
"""Return final result."""
return str(process_data)
Pipeline Config Template
params:
input_config:
source: "data.csv"
run:
final_vars:
- final_result
executor:
type: threadpool
max_workers: 4
retry:
max_retries: 3
retry_delay: 1.0
Running Pipelines
# Basic run
flowerpower pipeline run my_pipeline
# With inputs
flowerpower pipeline run my_pipeline --inputs '{"key": "value"}'
# With executor
flowerpower pipeline run my_pipeline --executor threadpool --executor-max-workers 8
# With retries
flowerpower pipeline run my_pipeline --max-retries 3 --retry-delay 2.0
Python API:
from flowerpower import FlowerPowerProject
project = FlowerPowerProject.load('.')
result = project.run('my_pipeline')
# With RunConfig
from flowerpower.cfg.pipeline.run import RunConfig
config = RunConfig(inputs={"key": "value"}, final_vars=["output"])
result = project.run('my_pipeline', run_config=config)
CLI Commands
| Command | Description |
|---|---|
flowerpower init --name <name> |
Initialize project |
flowerpower pipeline new <name> |
Create pipeline |
flowerpower pipeline run <name> |
Run pipeline |
flowerpower pipeline show-pipelines |
List pipelines |
flowerpower pipeline show-dag <name> |
Visualize DAG |
flowerpower pipeline delete <name> |
Delete pipeline |
Executor Types
| Type | Use Case | Config |
|---|---|---|
synchronous |
Default, sequential | - |
threadpool |
I/O-bound tasks | max_workers: N |
processpool |
CPU-bound tasks | max_workers: N |
ray |
Distributed computing | num_cpus: N |
dask |
Distributed computing | num_cpus: N |
Optional Dependencies
uv pip install flowerpower[io] # I/O plugins
uv pip install flowerpower[ui] # Hamilton UI
uv pip install flowerpower[all] # All extras
Resources
- references/overview.md - Key concepts, architecture, project structure
- references/configuration.md - Complete YAML configuration patterns
- references/hamilton-patterns.md - Hamilton function decorators and patterns
Scripts
- scripts/init_project.py - Initialize new flowerpower project
- scripts/create_pipeline.py - Create new pipeline with template
- scripts/run_pipeline.py - Execute pipeline with options
- scripts/list_pipelines.py - List available pipelines
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