test-automator

Overview of test-automator skill

The test-automator skill is a lightweight testing assistant for people who want an AI agent to draft tests, improve coverage, or set up a basic testing workflow without starting from a blank page. It is best for developers, QA-minded engineers, and repo maintainers who already know the code they want to protect but want faster test planning and test generation.

What test-automator is best at

The core job of the test-automator skill is to turn a request like “write tests for this module” into a more structured testing response based on a simple pyramid: many unit tests, fewer integration tests, and only selective end-to-end coverage. That makes it more useful than a generic prompt when you want the agent to think in terms of test scope, behavior coverage, mocking choices, and maintainable test naming.

Who should install test-automator

Install test-automator if you regularly ask an agent to:

• write unit tests for existing code
• improve weak or missing test coverage
• suggest integration vs unit test boundaries
• scaffold a test plan before implementation
• review mocking strategy and test determinism

It is especially practical for mixed-language teams because the repository explicitly mentions common frameworks across JavaScript/TypeScript, Python, Go, and Java.

What differentiates this skill from ordinary prompts

The main advantage of test-automator for Test Automation is not deep framework automation or CI orchestration. It is the opinionated testing guidance around:

• behavior-focused tests rather than implementation-chasing tests
• deterministic test design
• realistic mocking boundaries
• descriptive naming and Arrange-Act-Assert structure
• quick helper scripts for test-plan and coverage-report templates

That makes it a good install if you want better first-pass test output quality with less prompting effort.

Important limits before you adopt

This is not a full testing platform. The repository evidence shows a concise skill plus reference docs and two small Python helper scripts. It does not appear to include framework-specific generators for every stack, CI integrations, or advanced project introspection logic. If you need highly automated repo-specific test generation with deep framework conventions enforced, treat test-automator as guidance and scaffolding rather than full automation.

How to Use test-automator skill

Install context for test-automator

The repository does not expose a skill-specific installer inside SKILL.md, so the practical install pattern is to add it from the collection repo:

npx skills add https://github.com/zhaono1/agent-playbook --skill test-automator

After install, the skill is designed to activate when you ask for writing tests, automating tests, improving coverage, or setting up a testing framework.

Read these files first

For a fast evaluation of test-automator usage, start here:

1. skills/test-automator/SKILL.md
2. skills/test-automator/README.md
3. skills/test-automator/references/best-practices.md
4. skills/test-automator/references/mocking.md
5. skills/test-automator/references/examples/unit-test-example.md
6. skills/test-automator/scripts/generate_test.py
7. skills/test-automator/scripts/coverage_report.py

That reading order tells you the activation scope first, then the testing philosophy, then the helper artifacts.

What input the skill needs to work well

The test-automator skill produces much better output when you give it concrete implementation context. Include:

• file path or pasted source code
• language and test framework
• current behavior expected from the code
• important edge cases
• dependencies that should be mocked or left real
• whether you want unit, integration, or end-to-end tests
• any repo conventions for naming, fixtures, or directories

Weak input:

• “Write tests for this.”

Strong input:

• “Write pytest unit tests for payments/refunds.py. Focus on valid refund creation, invalid currency, network timeout from the gateway, and idempotency. Mock external HTTP calls but keep internal validation real. Use AAA structure and descriptive test names.”

How to turn a rough goal into a usable prompt

A practical test-automator guide prompt usually has five parts:

1. target code
2. framework
3. test scope
4. mocking rules
5. success criteria

Example:

“Use test-automator to create Vitest unit tests for src/user/createUser.ts. Test behavior, not private helpers. Cover success, invalid email, duplicate user, and repository failure. Mock outbound email delivery but do not mock validation logic. Return the test file plus a short note on remaining integration risks.”

That prompt is better because it constrains the agent to the right level of abstraction and prevents over-mocking.

Supported ecosystems and likely fit

The repo README explicitly calls out these pairings:

• TypeScript/JS: Jest, Vitest, Mocha
• Python: pytest, unittest
• Go: built-in testing
• Java: JUnit

This means test-automator install makes most sense when your project already uses one of those common frameworks. If your stack uses a niche framework, the skill can still help with test design, but you may need to adapt the syntax yourself.

Suggested workflow for real projects

A high-signal workflow for test-automator usage is:

1. ask the agent for a test plan first
2. review unit vs integration split
3. generate the first test file
4. run tests locally
5. fix mismatches between assumptions and actual code
6. ask for missing edge cases or coverage improvements
7. create a coverage report or action list

This is better than asking for “full coverage” in one step, because the skill’s value is strongest when the testing boundary is clarified first.

Use the helper scripts when planning work

The included scripts are simple but useful for team workflows.

Generate a test plan template:

python scripts/generate_test.py --name "Refunds API" --owner "payments-team"

Generate a coverage report template:

python scripts/coverage_report.py --name "billing-service" --owner "qa-platform"

These scripts do not analyze your codebase automatically. They generate editable markdown templates, which is still useful when you want the agent and humans aligned on scope, owners, scenarios, and low-coverage follow-up work.

What the skill emphasizes in test design

The strongest recurring guidance in the repository is:

• test behavior, not implementation
• prefer deterministic tests
• avoid order dependencies
• use explicit fixtures
• mock external services
• avoid mocking internal logic
• use realistic data shapes

If you follow those rules when prompting, the output from test-automator for Test Automation is more likely to survive refactors and fail for meaningful reasons.

Where users often get poor results

Most weak results come from underspecified requests, such as:

• no target framework named
• no code provided
• no distinction between unit and integration goals
• asking for tests around unstable or unclear behavior
• requesting mocks for everything, including business logic
• not sharing current failures or desired assertions

If the first output feels generic, that usually reflects a generic prompt, not a broken skill.

A practical prompt pattern to reuse

Use this reusable structure for test-automator usage:

“Use test-automator for <framework> on <file/module>. Create <unit/integration> tests for <behaviors>. Mock <external systems> but keep <internal logic> real. Include edge cases for <cases>. Follow <repo conventions>. Return the test file and a short explanation of coverage gaps.”

That pattern usually yields cleaner, more reviewable output than a vague “add tests.”

test-automator skill FAQ

Is test-automator good for beginners

Yes, if you already know the code under test. The test-automator skill keeps the advice simple and practical: testing pyramid, AAA structure, descriptive naming, deterministic tests, and mocking boundaries. It is suitable for beginners who need structure, but it will not replace understanding the application behavior.

When should I use test-automator instead of a normal prompt

Use test-automator when you want the agent to consistently frame the task as test engineering rather than generic code writing. The difference is most noticeable when deciding what to mock, what level of test to write, and how to cover behavior without coupling tests to internals.

Is test-automator only for unit tests

No. The repository explicitly references unit, integration, and end-to-end levels through the testing pyramid and the generated test-plan template. In practice, it is strongest for unit-test planning and generation, then useful for organizing broader coverage work.

Does test-automator automatically inspect coverage

Not directly. The included scripts/coverage_report.py creates a markdown coverage report template; it does not calculate real coverage metrics from your tooling. If you need actual instrumentation, keep using your framework’s coverage tools and use this skill to interpret gaps and plan follow-up tests.

Can test-automator generate framework-perfect tests every time

No. The test-automator guide should be treated as a strong drafting aid, not a guarantee of repo-perfect syntax or conventions. Expect to refine imports, fixtures, mocking APIs, and path setup based on your project.

When is test-automator a poor fit

Skip test-automator install if you mainly need:

• browser automation infrastructure
• CI pipeline authoring
• deep property-based testing support
• performance/load test tooling
• framework-specific plugins with rich codebase introspection

It is a better fit for test creation guidance and structured drafting than for full-stack testing platform automation.

How to Improve test-automator skill

Give test-automator behavior-first requirements

The single best way to improve test-automator output is to describe observable behavior, not internal functions you happen to see in the file. For example, ask for “reject invalid email and preserve existing users” rather than “call validator and repo helper methods.” This aligns with the skill’s strongest principle and leads to less brittle tests.

Specify test level and mock boundaries

Say upfront whether you want unit, integration, or end-to-end coverage. Also state what must be mocked:

• external APIs
• databases
• message queues
• filesystem
• time/randomness

And what should stay real:

• validation logic
• mapping logic
• business rules

This prevents the common failure mode where the agent writes tests that technically pass but verify almost nothing.

Share current repo conventions

If your repository uses specific patterns, tell the skill:

• test file naming
• fixture factories
• assertion style
• async test helpers
• directory layout
• coverage thresholds

The test-automator skill is much more effective when grounded in local conventions rather than generic defaults.

Ask for edge cases explicitly

Users often care most about the non-happy paths. If you omit them, the first draft will often be too optimistic. Name the cases directly:

• invalid input
• null or missing values
• retries and timeouts
• duplicate records
• permission failures
• partial upstream failures

This increases practical coverage much more than asking for “more tests.”

Iterate with execution feedback

After the first draft, run the tests and feed the errors back into test-automator usage. Good follow-up prompt:

“Use test-automator to fix these failing pytest tests. Keep the intended behavior the same. Here is the stack trace and the actual fixture setup.”

Execution feedback helps the agent correct imports, setup assumptions, and mock usage faster than asking for a full rewrite.

Use the planning artifacts to guide better outputs

Before generating lots of tests, create