Preface¶

Writing unit tests is an unavoidable part of the development workflow, yet it’s a task many people try to put off for as long as possible. Unclear function boundaries, untested edge cases, and mismatched framework choices often make writing tests more time-consuming than writing business code. An even more common scenario: after code has been merged into the main branch and coverage targets start pressing down on your team, you’ll find yourself staring at empty test_*.py or *.test.ts files in your IDE, unsure where to start.

AI programming assistants can help you “write a few tests”, but the quality of the output often depends on how you describe your requirements during the conversation. Sometimes only the happy path is covered, and sometimes the generated code doesn’t align with your project’s existing conventions. The idea behind Agent Skill is to codify “how to analyze code, choose a framework, and organize test reports” into a reusable SKILL.md, so that every time an Agent is triggered, it follows the same standardized workflow.

The unit-test-generator introduced in this article comes from the examples/ sample set in the community repository JackyST0/awesome-agent-skills. It is a Skill template for “generating unit tests from source code”: with a clear structure and low barrier to entry, it is an excellent starting point for learning how to write Skills or quickly rolling out a standardized testing workflow within your team. This Skill is licensed under CC0-1.0, so you can freely copy and modify it.

What is this¶

unit-test-generator is an Agent Skill instruction pack, with its core file being SKILL.md in the directory. After reading this file, the Agent will, when a user requests “generate unit tests”, “write tests for a function/class”, “improve code coverage”, etc., analyze the source code in fixed steps and output test code along with coverage explanations.

It is not tied to any single AI product. The same Skill directory can be used in tools that support the Agent Skills specification, such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, etc. (see below for installation paths for each platform). The Skill itself does not include a standalone test runner or CLI; its value lies in standardizing the test generation analysis workflow and reducing the time spent repeatedly explaining frameworks, boundaries, and output formats in every conversation.

Core Features and Highlights¶

According to the official SKILL.md, the Skill’s workflow can be summarized in five steps:

1. Identify code — Determine the programming language and code structure (functions, classes, modules).
2. Analyze functionality — Clarify inputs, outputs, and core behaviors.
3. Define boundaries — Identify boundary values and edge cases (null values, zeros, out-of-range values, etc.).
4. Select framework — Match the project’s commonly used testing framework based on the language.
5. Generate tests — Output runnable test cases and include coverage explanations.

Supported Languages and Testing Frameworks¶

The official documentation lists the following matching pairs:

It should be noted that: the Skill guides the Agent to make selections and generate code through instructions, it does not automatically detect installed dependencies in your repository. If your project already uses a fixed framework (for example, a full-stack monorepo unified with Vitest), specifying the framework name during the conversation will make the output better aligned with your existing engineering setup.

Covered Test Types¶

Test cases will be organized into four categories during generation:

• Happy path tests — Verify standard behavior for expected inputs.
• Boundary condition tests — Extreme values, critical states.
• Exception handling tests — Error branches, error types and messages.
• Null / empty input tests — null, undefined, empty strings, empty collections, etc.

Structured Output Report¶

The Skill requires results to be formatted using the templates/test-report.md template in the same directory. The template includes: analysis overview, list of functions under test, generated test code, case count statistics by category, and command placeholders for “how to run tests”. This ensures the Agent’s output is not just scattered code blocks, but a test generation report that can be archived and reviewed.

For beginners, this sample Skill has two additional practical values: first, it is community-maintained and replicable — the directory only contains SKILL.md and templates, with no complex scripts; second, it addresses high-frequency scenarios — writing tests is one of the most commonly requested AI programming tasks, and固化 it as a Skill is more efficient than manually writing long prompt words every time.

Installation and Activation¶

Method 1: One-click installation script from awesome-agent-skills (Recommended)¶

The repository provides cross-platform installation scripts that can download unit-test-generator along with SKILL.md and templates/test-report.md to the corresponding platform directory.

macOS / Linux:

# Interactive selection of platform and Skill
curl -sL https://raw.githubusercontent.com/JackyST0/awesome-agent-skills/main/install.sh | bash

# Install directly to Cursor
curl -sL https://raw.githubusercontent.com/JackyST0/awesome-agent-skills/main/install.sh | bash -s -- -p cursor -s unit-test-generator

Windows (PowerShell):

irm https://raw.githubusercontent.com/JackyST0/awesome-agent-skills/main/install.ps1 | iex

After installation, the Skill will appear in the target platform’s skills directory, for example, the global Cursor path is ~/.cursor/skills/unit-test-generator/.

Method 2: Manual copy¶

git clone https://github.com/JackyST0/awesome-agent-skills.git
cp -r awesome-agent-skills/examples/unit-test-generator ~/.cursor/skills/

If you only want to make it available for the current project, you can copy it to .cursor/skills/unit-test-generator/ (or .codex/skills/, .claude/skills/, etc., depending on the tool) in the project root directory, making it easy to share with the repository and your team.

Directory Locations in Various AI Programming Tools¶

According to the awesome-agent-skills official README and usage guide:

Project-level Skills take precedence over global Skills with the same name. After installation, you can confirm whether SKILL.md and templates/test-report.md exist by running ls ~/.cursor/skills/unit-test-generator/; if the Agent does not automatically detect it, restart your IDE or explicitly reference the Skill name in the conversation.

Typical Usage Example¶

The official SKILL.md provides a minimal reproducible example. Suppose you have the following Python function to test:

def divide(a: float, b: float) -> float:
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

In an Agent conversation, you can trigger the workflow like this (natural language is fine, no need to memorize fixed prompts):

Please generate unit tests for the divide function below using pytest, and output the report according to the test-report template.

[paste the source code above]

The typical output structure under the guidance of the Skill is as follows (excerpted from the official example):

import pytest
from your_module import divide

class TestDivide:
    """Tests for the divide function."""

    def test_divide_positive_numbers(self):
        assert divide(10, 2) == 5.0
        assert divide(7, 2) == 3.5

    def test_divide_negative_numbers(self):
        assert divide(-10, 2) == -5.0
        assert divide(10, -2) == -5.0
        assert divide(-10, -2) == 5.0

    def test_divide_by_zero_raises_error(self):
        with pytest.raises(ValueError, match="Cannot divide by zero"):
            divide(10, 0)

    def test_divide_zero_numerator(self):
        assert divide(0, 5) == 0.0

    def test_divide_float_precision(self):
        assert divide(1, 3) == pytest.approx(0.333333, rel=1e-5)

The “test coverage explanation” in the report will check items such as normal division, negative numbers, division by zero exception, zero numerator, floating-point precision, etc. After saving the generated file to the project’s test directory, run it according to your project’s conventions, for example:

pytest tests/test_divide.py -v

For JavaScript projects, simply specify “use Jest / Vitest” in your prompt and include the path to the module under test, and the Agent will generate corresponding syntax based on the language-framework table in the Skill.

Applicable Scenarios and Notes¶

Who and What Scenarios It Fits¶

• Adding tests to legacy code: Quickly create boundary and exception test cases for old modules that lack unit tests.
• TDD assistance for new functions: After implementing a function, have the Agent generate a first draft according to the Skill workflow, then manually prune and merge it.
• Unifying team output formats: Use the test-report.md template to ensure consistent test deliverables across different colleagues and conversations.
• Learning Skill writing: The sample has a small footprint and no external dependencies, making it ideal for forking and adding your team’s naming conventions, Mock conventions, or coverage thresholds.

Limitations and Recommendations When Using¶

1. Generated results require manual review. The Skill cannot guarantee that tests will pass, nor can it replace understanding of business semantics; you should run the test suite locally before merging.
2. Import paths and Mocks need to align with the project structure. The from your_module import divide in the official example is just a placeholder. You need to specify the real module path in the conversation, or manually modify the import after generation.
3. Unlisted languages require manual extension. The current Skill only covers Python, JS/TS, Java, Go, and Rust; if you use C#, Kotlin, etc., you can add framework entries in the forked SKILL.md or directly specify the framework in the conversation.
4. Align with project testing specifications: If your team requires Given-When-Then naming, prohibits real network requests, etc., it is recommended to add these rules to the Instructions section of the Skill instead of repeating them verbally every time.
5. Scope of the one-click installation script: install.sh installs several sample Skills bundled under examples/ in this repository, not a universal Skill package manager; third-party Skills still need to be copied manually or installed using each platform’s native installation method.

Summary¶

unit-test-generator encapsulates the unit test generation workflow of “analyze source code → select framework → cover boundaries and exceptions → output structured report” into an installable Agent Skill. It comes from a community-curated sample, is lightweight and replicable, and makes an excellent starting point for standardizing test writing in your AI programming workflow.

If you want to further customize it, you can directly fork examples/unit-test-generator and add your project’s directory conventions, coverage targets, or CI commands to SKILL.md; you can also browse other examples in the same repository, such as code-review and debug-helper, to build a team-specific set of Agent capabilities.

Official address: https://github.com/JackyST0/awesome-agent-skills/tree/main/examples/unit-test-generator