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Generating Cli Tests

OrganizationPopular
https-deeplearning-ai
generating-cli-tests

Generate pytest tests for Typer CLI commands. Includes fixtures (temp_storage, sample_data), CliRunner patterns, confirmation handling (y/n/--force), and edge case coverage. Use when user asks to "write tests for", "test my CLI", "add test coverage", or any CLI + test request.

Overview

Publisherhttps-deeplearning-ai
Repositorysc-agent-skills-files
Skill namegenerating-cli-tests
Stars
1.4K
Forks
614
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by https-deeplearning-ai on GitHub. Read the source before you install it.

Installation

Install the Generating Cli Tests AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/https-deeplearning-ai/sc-agent-skills-files/tree/main/L6/.claude/skills/generating-cli-tests
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/https-deeplearning-ai/sc-agent-skills-files.git /tmp/sc-agent-skills-files
mkdir -p .claude/skills
cp -r /tmp/sc-agent-skills-files/L6/.claude/skills/generating-cli-tests .claude/skills/generating-cli-tests
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Generating Cli Tests in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Generating Cli Tests on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Generating Cli Tests is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Generating CLI Tests

Patterns for generating tests for Typer CLI commands.

Workflow

  1. Identify command type (Create/Read/Update/Delete/Bulk)
  2. Ensure fixtures exist in conftest.py
  3. Write tests using scenarios below
  4. Run tests to verify

Fixtures (conftest.py)

python
import json
import pytest
from typer.testing import CliRunner


@pytest.fixture
def runner():
    """CLI test runner."""
    return CliRunner()


@pytest.fixture
def temp_storage(tmp_path, monkeypatch):
    """Empty storage for testing."""
    storage_dir = tmp_path / ".task"
    storage_dir.mkdir()
    storage_file = storage_dir / "tasks.json"
    storage_file.write_text(json.dumps({"version": 1, "tasks": []}))
    monkeypatch.setenv("TASK_STORAGE_PATH", str(storage_file))
    return storage_file


@pytest.fixture
def sample_data(temp_storage):
    """Pre-populated storage."""
    data = {
        "version": 1,
        "tasks": [
            {"title": "First task", "done": False, "priority": "low", "created_at": "2025-01-01T10:00:00", "due_date": None},
            {"title": "Second task", "done": True, "priority": "high", "created_at": "2025-01-01T11:00:00", "due_date": None},
        ]
    }
    temp_storage.write_text(json.dumps(data))
    return data

Test Structure (AAA)

python
def test_<command>_<scenario>(runner, temp_storage):
    # Arrange - via fixtures

    # Act
    result = runner.invoke(app, ["<command>", "<args>"])

    # Assert
    assert result.exit_code == 0
    assert "<expected>" in result.output

CliRunner Usage

python
from typer.testing import CliRunner
from task.main import app

runner = CliRunner()

# Basic
result = runner.invoke(app, ["add", "New task"])

# With options
result = runner.invoke(app, ["add", "Task", "--priority", "high"])

# With confirmation
result = runner.invoke(app, ["clear", "1"], input="y\n")  # Accept
result = runner.invoke(app, ["clear", "1"], input="n\n")  # Decline

# Skip confirmation
result = runner.invoke(app, ["clear", "1", "--force"])

Test Scenarios by Command Type

Create/Add

python
class TestAdd:
    def test_adds_task(self, runner, temp_storage):
        result = runner.invoke(app, ["add", "New task"])
        assert result.exit_code == 0
        assert "Added" in result.output

    def test_with_priority(self, runner, temp_storage):
        result = runner.invoke(app, ["add", "Task", "--priority", "high"])
        assert result.exit_code == 0

    def test_empty_title_shows_error(self, runner, temp_storage):
        result = runner.invoke(app, ["add", ""])
        assert result.exit_code == 2

Read/List

python
class TestList:
    def test_shows_tasks(self, runner, sample_data):
        result = runner.invoke(app, ["list"])
        assert result.exit_code == 0
        assert "First task" in result.output

    def test_empty_state(self, runner, temp_storage):
        result = runner.invoke(app, ["list"])
        assert "No tasks" in result.output or "empty" in result.output.lower()

    def test_with_filter(self, runner, sample_data):
        result = runner.invoke(app, ["list", "--done"])
        assert result.exit_code == 0

Update/Done

python
class TestDone:
    def test_marks_done(self, runner, sample_data):
        result = runner.invoke(app, ["done", "1"])
        assert result.exit_code == 0

    def test_not_found(self, runner, temp_storage):
        result = runner.invoke(app, ["done", "999"])
        assert result.exit_code == 1
        assert "not found" in result.output.lower()

Delete/Clear

python
class TestClear:
    def test_confirmed(self, runner, sample_data):
        result = runner.invoke(app, ["clear", "1"], input="y\n")
        assert result.exit_code == 0
        assert "Deleted" in result.output

    def test_declined(self, runner, sample_data):
        result = runner.invoke(app, ["clear", "1"], input="n\n")
        assert "Cancelled" in result.output

    def test_force(self, runner, sample_data):
        result = runner.invoke(app, ["clear", "1", "--force"])
        assert result.exit_code == 0

    def test_not_found(self, runner, temp_storage):
        result = runner.invoke(app, ["clear", "999", "--force"])
        assert result.exit_code == 1

Edge Cases to Cover

CategoryTest Cases
Invalid InputEmpty string, wrong type, out of range
Not FoundID doesn't exist
BoundaryZero, negative, first/last item
StateAlready done, empty storage
ConfirmationAccept (y), decline (n), force flag

Checklist

  • Test file: tests/test_<command>.py
  • Fixtures in conftest.py
  • Uses CliRunner from typer.testing
  • AAA structure (Arrange, Act, Assert)
  • Tests exit codes: 0, 1, 2
  • Destructive commands: tests y/n and --force
  • Output assertions check expected messages

Running Tests

bash
uv run pytest                       # All tests
uv run pytest -v                    # Verbose
uv run pytest tests/test_add.py    # Specific file

Frequently asked questions

What does the Generating Cli Tests AI skill do?

Generate pytest tests for Typer CLI commands. Includes fixtures (temp_storage, sample_data), CliRunner patterns, confirmation handling (y/n/--force), and edge case coverage. Use when user asks to "write tests for", "test my CLI", "add test coverage", or any CLI + test request.

Why use Generating Cli Tests on TypingMind?

Because you install it once and use it with any model. Generating Cli Tests is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Generating Cli Tests in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/https-deeplearning-ai/sc-agent-skills-files/tree/main/L6/.claude/skills/generating-cli-tests. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Generating Cli Tests?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Generating Cli Tests?

As many as you like. As long as a model supports skills, you can use Generating Cli Tests with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Generating Cli Tests AI skill free?

It is published on GitHub by https-deeplearning-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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