Agent skill

Testing

by seb1n in seb1n/awesome-ai-agent-skills

Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting.

MITAuto-check passedTesting & QA

Install Testing

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill testing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills testing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/code-and-development/testing .claude/skills/testing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
testing
GitHub stars
206
Token cost
~2.3k tokens
SKILL.md length
773 words
Files
1
Skills in repo
92
Repo updated
First seen
Licence
MIT

At a glance

Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting.

  • Works in 6 steps: Analyze the source code. Read the target… → Identify test cases. For each function… → Write the tests. Generate… → …
  • The user requests testing
  • SKILL.md covers Workflow, Supported Languages, Usage and Examples, plus 2 more sections
  • Calls pytest, npx and jest

What it does

Testing is an agent skill from seb1n/awesome-ai-agent-skills. Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting. Use when the user requests testing or provides relevant inputs for this workflow.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Unit testing and End-to-end testing. It works with Jest and pytest. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests testing
  • Provides relevant inputs for this workflow

Example prompts

  • “/testing”

Requirements

  • Python 3
  • Node.js

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Analyze the source code. Read the target file or module and build a dependency graph of its functions, classes, and external interactions…
  2. Identify test cases. For each function or method, enumerate the scenarios that need coverage: happy-path inputs, boundary values, invalid…
  3. Write the tests. Generate well-structured test code using the project's existing test framework (e.g., pytest, Jest, JUnit). Each test…
  4. Run the tests. Execute the test suite using the appropriate runner command. Capture the full output including pass/fail status, assertion…
  5. Analyze coverage. Run the test suite with coverage instrumentation enabled (e.g., pytest --cov, jest --coverage). Parse the coverage…
  6. Suggest improvements. Based on coverage gaps and code complexity, recommend additional test cases. Suggest refactoring opportunities that…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pytest
    • npx
    • jest
    • mvn
    • go
    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Testing loads about 2.3k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 773 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 773 words, ~2,305 tokens.

Download SKILL.mdSave it as .claude/skills/testing/SKILL.md (or your agent's skills folder).
name
testing
description
Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting. Use when the user requests testing or provides relevant inputs for this workflow.
license
MIT
metadata.author
awesome-ai-agent-skills contributors
metadata.version
1.0.0

Testing

This skill enables an AI agent to systematically generate, run, and evaluate tests for a given codebase. It covers the full testing lifecycle — from analyzing source code and identifying meaningful test cases, through writing and executing tests, to measuring coverage and recommending improvements. The agent supports unit tests, integration tests, and end-to-end tests across multiple languages and frameworks.

Workflow

  1. Analyze the source code. Read the target file or module and build a dependency graph of its functions, classes, and external interactions. Identify public interfaces, internal helpers, input parameters, return types, and side effects. This step determines what is testable and what kinds of tests are appropriate.

  2. Identify test cases. For each function or method, enumerate the scenarios that need coverage: happy-path inputs, boundary values, invalid or null inputs, exception paths, and state transitions. For integration points, identify the collaborators that need to be mocked or stubbed versus tested live. Prioritize cases by risk — complex branching logic and public API surfaces come first.

  3. Write the tests. Generate well-structured test code using the project's existing test framework (e.g., pytest, Jest, JUnit). Each test should have a descriptive name that states the scenario and expected outcome. Use the Arrange-Act-Assert pattern: set up preconditions, invoke the code under test, and assert the expected result. Add parameterized tests where a single logical case applies to multiple input sets.

  4. Run the tests. Execute the test suite using the appropriate runner command. Capture the full output including pass/fail status, assertion messages, and timing information. If any tests fail, parse the failure output to determine whether the failure indicates a bug in the source code or an error in the test itself.

  5. Analyze coverage. Run the test suite with coverage instrumentation enabled (e.g., pytest --cov, jest --coverage). Parse the coverage report to identify uncovered lines, branches, and functions. Flag any critical code paths — error handlers, security checks, data validation — that lack coverage.

  6. Suggest improvements. Based on coverage gaps and code complexity, recommend additional test cases. Suggest refactoring opportunities that would make the code more testable, such as extracting pure functions or introducing dependency injection. Provide a summary report with coverage percentages and a prioritized list of next actions.

Supported Languages

LanguageFrameworkRunner Command
Pythonpytestpytest --cov=src -v
JavaScriptJestnpx jest --coverage --verbose
TypeScriptJest / Vitestnpx vitest run --coverage
JavaJUnit 5mvn test
Gotesting (stdlib)go test -cover ./...
Rustcargo testcargo test

Usage

Provide one or more of the following inputs:

  • Source file or directory to generate tests for (e.g., src/utils/parser.py).
  • Existing test file if you want the agent to extend or improve current tests.
  • Test framework preference if the project does not already have one configured.
  • Coverage threshold if you want the agent to target a specific percentage (e.g., 90%).
Show full SKILL.md (307 more words)Show less

Examples

Example 1 — Python with pytest

Given this source file src/cart.py:

python
class ShoppingCart:
    def __init__(self):
        self.items = []

    def add_item(self, name: str, price: float, quantity: int = 1):
        if price < 0:
            raise ValueError("Price cannot be negative")
        if quantity < 1:
            raise ValueError("Quantity must be at least 1")
        self.items.append({"name": name, "price": price, "quantity": quantity})

    def total(self) -> float:
        return sum(item["price"] * item["quantity"] for item in self.items)

    def remove_item(self, name: str):
        self.items = [item for item in self.items if item["name"] != name]

The agent generates tests/test_cart.py:

python
import pytest
from src.cart import ShoppingCart

@pytest.fixture
def cart():
    return ShoppingCart()

def test_add_item_and_total(cart):
    cart.add_item("Widget", 9.99, 2)
    assert cart.total() == pytest.approx(19.98)

def test_empty_cart_total_is_zero(cart):
    assert cart.total() == 0.0

def test_add_item_negative_price_raises(cart):
    with pytest.raises(ValueError, match="Price cannot be negative"):
        cart.add_item("Bad", -1.0)

def test_add_item_zero_quantity_raises(cart):
    with pytest.raises(ValueError, match="Quantity must be at least 1"):
        cart.add_item("Bad", 5.0, 0)

def test_remove_item(cart):
    cart.add_item("A", 1.0)
    cart.add_item("B", 2.0)
    cart.remove_item("A")
    assert cart.total() == 2.0

def test_remove_nonexistent_item_does_nothing(cart):
    cart.add_item("A", 1.0)
    cart.remove_item("Z")
    assert cart.total() == 1.0

Run: pytest tests/test_cart.py --cov=src -v

Example 2 — JavaScript with Jest

Given this source file src/validator.js:

javascript
function isValidEmail(email) {
  if (typeof email !== "string") return false;
  const re = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
  return re.test(email);
}

function isStrongPassword(password) {
  if (typeof password !== "string") return false;
  return (
    password.length >= 8 &&
    /[A-Z]/.test(password) &&
    /[a-z]/.test(password) &&
    /[0-9]/.test(password)
  );
}

module.exports = { isValidEmail, isStrongPassword };

The agent generates src/__tests__/validator.test.js:

javascript
const { isValidEmail, isStrongPassword } = require("../validator");

describe("isValidEmail", () => {
  test.each([
    ["user@example.com", true],
    ["name+tag@sub.domain.org", true],
    ["missing-at-sign.com", false],
    ["@no-local.com", false],
    ["spaces in@email.com", false],
    ["", false],
  ])("isValidEmail(%s) => %s", (input, expected) => {
    expect(isValidEmail(input)).toBe(expected);
  });

  test("returns false for non-string input", () => {
    expect(isValidEmail(null)).toBe(false);
    expect(isValidEmail(42)).toBe(false);
  });
});

describe("isStrongPassword", () => {
  test("accepts a strong password", () => {
    expect(isStrongPassword("Str0ngPwd")).toBe(true);
  });

  test("rejects short password", () => {
    expect(isStrongPassword("Ab1")).toBe(false);
  });

  test("rejects password without uppercase", () => {
    expect(isStrongPassword("alllower1")).toBe(false);
  });

  test("rejects non-string input", () => {
    expect(isStrongPassword(undefined)).toBe(false);
  });
});

Run: npx jest --coverage --verbose

Best Practices

  • Name tests after the scenario, not the implementation. Use names like test_empty_cart_total_is_zero rather than test_total_method. This makes failures self-documenting.
  • Keep tests independent. Each test should set up its own state via fixtures or setup methods. Never rely on test execution order.
  • Prefer parameterized tests for input variations. When the same logic applies to many inputs, use @pytest.mark.parametrize or test.each instead of duplicating test bodies.
  • Mock external dependencies, not internal logic. Stub network calls, databases, and file I/O. Avoid mocking the code under test itself — that defeats the purpose.
  • Target meaningful coverage, not 100%. Aim for thorough branch coverage of critical paths. Trivial getters and framework-generated code rarely need dedicated tests.
  • Run tests in CI on every commit. Integrate the test command into the project's CI pipeline so regressions are caught immediately.

Edge Cases

  • Dynamically generated code: If the codebase uses metaprogramming, decorators, or code generation, the agent may not detect all callable paths. Provide hints about generated interfaces.
  • Global state and singletons: Tests for code that mutates global state require careful teardown. The agent will flag these but may need guidance on acceptable reset strategies.
  • Async and concurrent code: Testing async functions requires framework-specific patterns (pytest-asyncio, Jest's async handling). The agent will use the appropriate pattern but may ask for confirmation on timeout thresholds.
  • Environment-dependent tests: Tests that depend on environment variables, file system layout, or network access should be clearly marked as integration tests and excluded from fast unit-test runs.
  • Flaky tests: If test runs produce intermittent failures, the agent will flag non-deterministic patterns (e.g., reliance on wall-clock time, unordered collection comparisons) and suggest fixes.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in code-and-development/testing of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

Testing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Testing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Testing this skillseb1n/awesome-ai-agent-skills206—~2.3kAutomated safety check: PassMIT
Designing TestsCloudAI-X/claude-workflow-v21.4k1 repos~1.5kAutomated safety check: PassMIT
E2E Agent Browserjh941213/my-cc-harness126—~3.1kAutomated safety check: NotesNone
Test Detectdavila7/claude-code-templates32k—~989Automated safety check: PassMIT
Error Explanation GeneratorArabelaTso/Skills-4-SE253—~3.8kAutomated safety check: PassApache-2.0
Discover Testingrand/cc-polymath181—~513Automated safety check: PassMIT

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Works with

Categories

Questions about Testing

What does Testing do?

Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting. Testing is an agent skill from seb1n/awesome-ai-agent-skills. Generate, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting.

When should I use Testing?

Testing fits situations like: the user requests testing; provides relevant inputs for this workflow.

How do I install Testing in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill testing -a claude-code`. Or copy the skill folder (code-and-development/testing in seb1n/awesome-ai-agent-skills) into .claude/skills/testing in your project. Claude Code loads it when a task matches its description.

How do I install Testing in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill testing -a codex`. Or copy the skill folder (code-and-development/testing in seb1n/awesome-ai-agent-skills) into .agents/skills/testing in your project. Codex loads it when a task matches its description.

Can I use Testing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/testing, .gemini/skills/testing, .github/skills/testing and .opencode/skills/testing in your project.

What does Testing need to run?

Going by SKILL.md and its folder, Testing needs the command-line tools its instructions call (pytest, npx, jest, mvn, go and cargo). Our summary lists: Python 3; Node.js.

Does Testing access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Testing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Testing use?

Testing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Testing use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Testing?

Skills that share tags, products or a category with Testing: Designing Tests (CloudAI-X/claude-workflow-v2, 1.4k stars), E2E Agent Browser (jh941213/my-cc-harness, 126 stars), Test Detect (davila7/claude-code-templates, 32k stars) and Error Explanation Generator (ArabelaTso/Skills-4-SE, 253 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Testing?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.