cliffy 0.6.0

$ cli load from.yaml

Build feature-rich CLIs quickly.

Features

• ⚡ Generate Python CLIs with a simple YAML manifest
• 📦 Package your CLI into a single executable that runs anywhere
• 🔄 Hot-reload changes instantly during development with built-in testing
• 🎨 Mix Python and shell commands naturally with built-in scripting support
• 🤖 AI-friendly with manifest generation support

Load

1. Define a manifest

# hello.yaml
name: hello
version: 0.1.0

commands:
  shell: 
    help: Print hello in shell
    run: $echo "hello from shell"
  python: print("hello from python")

2. Load CLI

$ cli load hello.yaml

Parses hello.yaml to generate a Typer CLI and load it into the running Python environment.

3. Run CLI directly

hello -h

Build

Simple todo CLI with sqlite3 + tabulate.

1. Define the manifest

# todo.yaml
name: todo
version: 1.0.0
requires:
  - tabulate  # For pretty table output
  - rich      # For colored terminal output
imports: |
  import sqlite3
  from pathlib import Path
  from tabulate import tabulate
  from rich import print

commands:
  create:
    help: Create a new database with tasks table
    params:
      - name: str = typer.Option(..., prompt=True, confirmation_prompt=True)
    run: |
      db_path = Path(f"{name}.db")
      conn = sqlite3.connect(db_path)
      conn.execute("CREATE TABLE tasks (id INTEGER PRIMARY KEY, task TEXT NOT NULL, done BOOLEAN NOT NULL)")

      # insert example tasks
      conn.execute("INSERT INTO tasks (task, done) VALUES ('Fight for your right!', 0)")
      conn.execute("INSERT INTO tasks (task, done) VALUES ('To party!', 1)")
      conn.commit()
      conn.close()
      print(f"✨ Created database {db_path} with tasks table")

  tasks:
    help: List tasks in database
    params: [name: str!]
    run: |
      conn = sqlite3.connect(f"{name}.db")
      cursor = conn.execute("SELECT * FROM tasks")
      tasks = cursor.fetchall()
      conn.close()
      print(tabulate(tasks, headers=['ID', 'Task', 'Done'], tablefmt='grid'))

  add:
    help: Add a new task
    params: [name: str!, task: str!]
    run: |
      conn = sqlite3.connect(f"{name}.db")
      conn.execute("INSERT INTO tasks (task, done) VALUES (?, 0)", (task,))
      conn.commit()
      conn.close()
      print(f"📝 Added task: {task}")

  complete:
    help: Mark a task as complete
    params: [name: str!, id: int!]
    run: |
      conn = sqlite3.connect(f"{name}.db")
      conn.execute("UPDATE tasks SET done = 1 WHERE id = ?", (id,))
      conn.commit()
      conn.close()
      print(f"🎉 Marked task {id} as complete")

For more examples, check examples directory.

Usage

cli <command>

Command	Description
init <cli name>	Generate a template CLI manifest for a new CLI
load <manifest>	Add a new CLI based on the manifest
render <manifest>	View generated CLI script for a manifest
list, ls	Output a list of loaded CLIs
update <cli name>	Reload a loaded CLI
remove <cli name>, rm <cli name>	Remove a loaded CLI
run <manifest> -- \<args>	Runs a CLI manifest command in isolation
build <cli name or manifest>	Build a CLI manifest or a loaded CLI into a self-contained zipapp
info <cli name>	Display CLI metadata
dev <manifest>	Start hot-reloader for a manifest for active development
test <manifest>	Run tests defined in a manifest
validate <manifest>	Validate the syntax and structure of a CLI manifest
docs <cli name or manifest>	Generate documentation for a CLI
ai generate <cli name> <description>	Generate a CLI manifest based on a description.
ai ask <prompt>	Ask a question about cliffy or a specific CLI manifest.

How it works

1. Define CLI manifests in YAML files
2. Run cli commands to load, build, and manage CLIs
3. When loaded, cliffy parses the manifest and generates a Typer CLI that is deployed directly as a script
4. Any code starting with $ will translate to subprocess calls via PyBash
5. Run loaded CLIs straight from the terminal
6. When ready to share, run build to generate portable zipapps built with Shiv

Get started

Cliffy can be installed using either pip or uv package managers.

With pip

• pip install "cliffy[rich]" to include rich-click for colorful CLI help output formatted with rich.

or

• pip install cliffy to use the default help output.
• cli init mycli

With uv

• Init: uvx cliffy init mycli
• Load: uvx cliffy load mycli.yaml
• Run: uvx --from cliffy mycli -h

Manifest template

Generated by cli init. For a barebones template, run cli init --raw

manifestVersion: v3

# The name of the CLI, used when invoking from command line.
name: cliffy

# CLI version
version: 0.1.0

# Brief description of the CLI
help: A brief description of your CLI

# List of Python package dependencies for the CLI.Supports requirements specifier syntax.
requires: []
  # - requests>=2.25.1
  # - pyyaml~=5.4

# List of external CLI manifests to include.Performs a deep merge of manifests sequentially in the order given to assemble a merged manifest
# and finally, deep merges the merged manifest with this manifest.
includes: []
  # - path/to/other/manifest.yaml

# Mapping defining manifest variables that can be referenced in any other blocks
# Environments variables can be used in this section with ${some_env_var} for dynamic parsing
# Supports jinja2 formatted expressions as values
# Interpolate defined vars in other blocks jinja2-styled {{ var_name }}.
vars:
  data_file: "data.json"
  debug_mode: "{{ env['DEBUG'] or 'False' }}"

# String block or list of strings containing any module imports
# These can be used to import any python modules that the CLI depends on.
imports: |
  import json
  import os
  from pathlib import Path

# List of helper function definitions
# These functions should be defined as strings that can be executed by the Python interpreter.
functions:
  - |
    def load_data() -> dict:
        data_path = Path("{{ data_file }}")
        if data_path.exists():
            with data_path.open() as f:
                return json.load(f)
        return {}
  - |
      def save_data(data):
          with open("{{data_file}}", "w") as f:
              json.dump(data, f, indent=2)
# A mapping containing any shared type definitions
# These types can be referenced by name in the args section to provide type annotations for params and options defined in the args section.
types:
  Filename: str = typer.Argument(..., help="Name of the file to process")
  Verbose: bool = typer.Option(False, "--verbose", "-v", help="Enable verbose output")

# Arguments applied to all commands
global_params:
  - verbose: Verbose

# Reusable command templates
command_templates:
  with_confirmation:
    params:
      - "yes": bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt")
    pre_run: |
      if not yes:
        typer.confirm("Are you sure you want to proceed?", abort=True)

# A mapping containing the command definitions for the CLI
# Each command should have a unique key- which can be either a group command or nested subcommands
# Nested subcommands are joined by '.' in between each level
# Aliases for commands can be separated in the key by '|'
# A special '(*)' wildcard can be used to spread the subcommand to all group-level commands
commands:
  hello:
    help: Greet the user
    params:
      - name: str = typer.Option("World", "--name", "-n", help="Name to greet")
    run: |
      print(f"Hello, {name}!")
      $ echo "i can also mix-and-match this command script to run shell commands"

  file.process:
    help: Process a file
    params:
      - filename: Filename
    run: |
      data = load_data()
      print(f"Processing {filename}")
      if verbose:
        print("Verbose output enabled")
      data["processed"] = [filename]
      # Process the file here
      save_data(data)

  delete|rm:
    help: Delete a file
    template: with_confirmation
    params: [filename: Filename]
    run: |
      if verbose:
        print(f"Deleting {filename}")
      os.remove(filename)
      print("File deleted successfully")

# Additional CLI configuration options
cli_options:
  rich_help_panel: True

# Test cases for commands
tests:
  - hello --name Alice: assert 'Hello, Alice!' in result.output
  - file process test.txt: assert 'Processing test.txt' in result.output