sprout-template 1.3.3

A Jinja2 project generator with Python-based configuration

sprout

Sprout is a Jinja2-based project generator with a Python manifest.

Instead of configuring prompts in YAML, you write sprout.py:

from sprout import Question

questions = [
    Question(key="project_name", prompt="Project name"),
]

That single manifest drives interactive prompts, CLI flags, validation and conditional questions.

Template files go in template/. .jinja files are rendered, everything else is copied.

Works with local templates, Git repos, or owner/repo GitHub shorthand.

Every question becomes a CLI flag so you can script it too:

sprout new <template-path> <project-path>
sprout new <template-path> <project-path> --project-name demo

Install

uv tool install sprout-template

Usage

sprout init [directory]
sprout add <template-source> [--name <trusted-name>]
sprout list
sprout new <template> <project-path> [--force] [--<question-flag> <value> ...]

new accepts a local template path, Git URL, owner/repo GitHub shorthand, or a trusted name added with sprout add. Pass values for question flags to skip those prompts:

sprout new <template-path> <project-path> --project-name demo

Use sprout new <template> --help to show template-specific flags.

Initialize a template

Create a minimal sprout.py and template/README.md.jinja scaffold in the current directory:

sprout init

Pass a directory to initialize it elsewhere. Existing scaffold files are never overwritten.

Trusted templates

Store a reusable name for any supported template source:

sprout add zigai/python-project-template --name python
sprout new python ./my-project
sprout list

Template structure

The source root must contain sprout.py.

The only required name is questions.

from sprout import Question

questions = [
    Question(key="project_name", prompt="Project name"),
]

Optional names are template_dir, style, extensions, title, cli_boolean_style, should_skip_file(...), and apply(context).

Question model

Each Question describes one answer:

from sprout import Question

Question(
    key="project_name",
    prompt="Project name",
    help="Used for package metadata and generated paths",
    default="demo",
)

key is the answer dictionary key. It also becomes the CLI flag name.

project_name -> --project-name

Choices

Use choices when the answer should come from a closed list:

from sprout import Question

questions = [
    Question(
        key="package_manager",
        prompt="Package manager",
        choices=[("uv", "uv"), ("pip", "pip")],
        default="uv",
    ),
]

Multiselect

from sprout import Question

questions = [
    Question(
        key="workflow",
        prompt="Workflows",
        choices=[("tests", "Tests"), ("lint", "Lint")],
        multiselect=True,
    ),
]

From the CLI:

sprout new <template-path> <project-path> --workflow tests --workflow lint

Booleans

Use the built-in yes/no helper:

from sprout import Question

questions = [
    Question.yes_no(
        key="git_init",
        prompt="Initialize Git?",
        default=True,
    ),
]

By default, yes/no questions are exposed as Boolean CLI flags:

sprout new <template-path> <project-path> --git-init
sprout new <template-path> <project-path> --no-git-init

If a template should use explicit yes/no values instead, opt into that style in sprout.py:

cli_boolean_style = "yes-no"

Then the CLI accepts values for yes/no questions:

sprout new <template-path> <project-path> --git-init yes
sprout new <template-path> <project-path> --git-init no

Conditional flow

when can be a boolean or a callable that receives the answers collected so far:

from sprout import Question

questions = [
    Question.yes_no(
        key="create_github_repo",
        prompt="Create GitHub repository?",
        default=False,
    ),
    Question(
        key="github_repo_visibility",
        prompt="GitHub repository visibility",
        choices=[("private", "Private"), ("public", "Public")],
        default="private",
        when=lambda answers: bool(answers.get("create_github_repo")),
    ),
]

Defaults, parsers, and validators

Defaults can be static values or callables. Use default="" for a text prompt that should accept a blank answer.

from sprout import Question

questions = [
    Question(
        key="package_name",
        prompt="Package name",
        default=lambda answers: str(answers["project_name"]).replace("-", "_"),
    ),
]

Validators return (valid, message):

from sprout import Question, validate_repository_url

questions = [
    Question(
        key="repository_url",
        prompt="Repository URL",
        validators=[validate_repository_url],
    ),
]

Sprout includes validators for repository URLs, GitHub repository URLs, repository names, npm package names, and semantic versions.

Destination-aware questions

When question definitions need runtime context, make questions callable:

from pathlib import Path
from jinja2 import Environment
from sprout import Question


def questions(env: Environment, destination: Path) -> list[Question]:
    return [
        Question(
            key="project_name",
            prompt="Project name",
            default=destination.name,
        ),
    ]

The callable must accept exactly two positional parameters: env and destination.

Rendering

Default rendering uses template_dir, or template when no directory is declared.

template_dir = "template"

Skipping files

should_skip_file receives a path relative to template_dir and the final answers:

from sprout import NO_LICENSE


def should_skip_file(relative_path: str, answers: dict[str, object]) -> bool:
    return relative_path == "LICENSE.jinja" and answers.get("license") == NO_LICENSE

Jinja environment

Set Jinja2 extension classes with extensions:

from sprout import CurrentYearExtension, GitDefaultsExtension

extensions = [GitDefaultsExtension, CurrentYearExtension]

When extensions is omitted, the default environment includes Git defaults:

• git_user_name
• git_user_email
• github_username

Include GitDefaultsExtension explicitly when you provide a custom extension list and still want those globals.

CurrentYearExtension exposes:

• current_year

Prompt title and style

title = "Generate a Python package"

from sprout import ManifestContext


def title(context: ManifestContext) -> str | None:
    return f"Generate project in {context.destination}"

The title is evaluated before answers are collected. For prompt appearance, assign style to a sprout.Style instance.

Custom generation

Most templates should use the default renderer, but you can define apply(context) when generation needs custom file creation, post-processing, or post-generation actions.

from sprout import ManifestContext, render_templates


def apply(context: ManifestContext):
    return render_templates(
        context.env,
        context.template_dir,
        context.destination,
        context.answers,
        render_paths=True,
    )

apply must accept exactly one context parameter. It may return None, one path, or a sequence of paths for the generated-files summary.

Examples

• python-project-template.

License

MIT License