How does this skill differ from standard AI code generation or basic test prompts?

Unlike standard tools that focus on code coverage, this skill uses a mutation-oriented approach to design tests that specifically target edge cases, boundary failures, and logic errors that typical 'happy path' generators miss.

Which programming languages and testing frameworks are supported?

The skill is language-agnostic and supports all major frameworks including Jest, Pytest, JUnit, and Vitest, adapting its output to match your project's specific mocking libraries and coding conventions.

What exactly is included with my purchase of the Unit & Integration Test Generator?

You receive the fully optimized AI agent skill, a set of configuration guidelines for different environments, and documentation on how to provide the best code context for high-quality test generation.

What is the setup process for integrating this skill into my workflow?

Simply import the skill into your preferred AI agent platform; once active, you can provide source code or API specifications to begin generating risk-ranked test plans and executable test code.

Can this skill help me fix existing tests that are brittle or provide false confidence?

The skill identifies potential 'flakiness' by auditing for over-mocking and brittle assertions, then suggests specific mutation ideas to ensure your tests fail correctly when the underlying logic is broken.

Unit & Integration Test Generator skill

Example session with this skill installed

Generate unit tests for this TypeScript function that calculates a loyalty discount. It takes a 'user' object and 'cartTotal'. Logic: 10% off if user is 'PRO', 5% if orders > 10. Max discount $50.

Read your context and instructions
Compiled the unit & integration

Analyzed loyalty discount logic. Identified risks in discount stacking, max cap boundaries, and missing user properties.

Test Suite: Loyalty Discount Calculator

import { calculateDiscount, User } from './loyalty';

describe('calculateDiscount', () => {
  // Behavior: PRO users get 10% discount
  test('should apply 10% discount for PRO status regardless of order count', () => {
    const user: Partial<User> = { status: 'PRO', orderCount: 2 };
    const result = calculateDiscount(user as User, 100);
    expect(result).toBe(10);
  });

  // Behavior: High volume users get 5% discount
  test('should apply 5% discount for users with > 10 orders', () => {
    const user: Partial<User> = { status: 'BASIC', orderCount: 11 };
    const result = calculateDiscount(user as User, 200);
    expect(result).toBe(10);
  });

  // Boundary: Max discount cap
  test('should cap the discount at $50 for large PRO orders', () => {
    const user: Partial<User> = { status: 'PRO', orderCount: 5 };
    const result = calculateDiscount(user as User, 1000);
    expect(result).toBe(50);
  });

  // Negative Path: No discount for low volume BASIC users
  test('should return 0 discount for BASIC users with exactly 10 orders', () => {
    const user: Partial<User> = { status: 'BASIC', orderCount: 10 };
    const result = calculateDiscount(user as User, 100);
    expect(result).toBe(0);
  });

  // Risk: Malformed input
  test('should return 0 for negative cart totals', () => {
    const user: Partial<User> = { status: 'PRO', orderCount: 15 };
    const result = calculateDiscount(user as User, -100);
    expect(result).toBe(0);
  });
});

Why these tests matter

Boundary Cap Test: Targeted at a mutation where the $50 constant is ignored or incorrectly compared.
Order Count 10 Test: Specifically catches off-by-one errors (e.g., using >= instead of >).

Next steps

Verify the User interface matches the Partial properties used in mocks.
Add an integration test to ensure cartTotal is rounded before discount calculation.

Connects securely to your tools. The creator never sees your data.

About this skill

Testing scripts often fail to catch real bugs because they focus on implementation details rather than observable behavior. This skill acts as a senior test engineer to generate unit and integration tests designed specifically to detect defects, not just increase coverage percentages.

What it does

Behavioral testing focuses on API contracts, invariants, and side effects rather than internal implementation.
Risk identification analyzes branches, boundaries, and state transitions to find high-probability failure points.
Multi-level generation produces both isolated unit tests and integration tests for real service boundaries.
Defect targeting explains exactly which plausible mutation or bug each test case is designed to catch.
Assertion auditing reviews tests for tautological checks or brittle mocks that create false confidence.

How it works

Analyze input to summarize the contract, preconditions, and postconditions of your code or API.
Draft a test plan ranking scenarios by risk, including happy paths, boundaries, and failure states.
Generate test code using your project's conventions, imports, and preferred test runner.
Audit for weaknesses by suggesting mutation ideas and identifying potential flakiness or over-mocking.

Frameworks & tools

Works with any language and test runner, including Jest, Vitest, Pytest, JUnit, Mocha, and Go testing. It adapts to specific project styles for mocking libraries and assertion patterns.

Why this beats prompting it yourself

Standard prompts often produce "happy path" tests that pass even when logic is broken. This skill uses mutation-oriented thinking to ensure assertions actually fail when behavior changes, preventing the creation of a suite that provides a false sense of security.

Use cases

Generate test suites for complex business logic with multiple edge cases.
Create integration tests for service boundaries and persistence layers.
Refactor brittle tests that break every time internal code is touched.
Build negative test cases for error handling and authorization logic.

Known limitations

Cannot execute tests or provide real coverage data without access to a runtime environment. It relies on the buyer to provide API contracts or source code for context.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Unit & Integration Test Generator skill