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Qe Test Generation

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proffesor-for-testing
qe-test-generation

Generates durable-first tests — invariants, contracts, and property-based tests at boundaries that survive a reimplementation — plus unit, integration, and e2e coverage. Use when creating tests for new or changed code, filling coverage gaps, or migrating test suites between Jest, Vitest, and Playwright.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameqe-test-generation
Stars
480
Forks
92
Bundled files
5
LicenseMIT
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Qe Test Generation 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.
  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/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/qe-test-generation .claude/skills/qe-test-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qe Test Generation 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 Qe Test Generation 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 Qe Test Generation 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.

QE Test Generation

Purpose

Guide the use of v3's AI-powered test generation capabilities including pattern-based test synthesis, multi-framework support, and intelligent test case derivation from code analysis.

Write durable-first (the core rule)

When AI makes code cheap to regenerate, the durable asset is the test that still holds after the implementation is thrown away and rewritten. Generate tests in durability tiers, and lead with the durable ones (ADR-113):

TierWhat it isSurvives a rewrite?When to write
DurableInvariants, contracts/schemas, property-based tests, behavioral e2e — specified at the module's public boundaryYesAlways — ≥1 per target
EphemeralExample-based unit tests, mock-call/interaction tests (TDD-London style)No (coupled to impl)For the red-green loop; label them, delete freely
LiveMonitoring / drift / cost assertions that run against realityContinuouslyFor deployed behavior

The language-swap heuristic: if reimplementing this module in another language would invalidate the test, the test is at the wrong boundary. Push it up a tier — assert on the observable contract, not on how the current code happens to work.

Every generated target MUST include at least one durable assertion (an invariant, a contract check, or a property). Mock-call assertions (toHaveBeenCalledWith) are ephemeral by definition — never let them be the only thing testing a target. Tag each generated test // @tier durable|ephemeral|live so its lifetime is explicit.

These tests are graded as oracles: a good test passes against the real code and fails against a seeded bug (mutant). A test that asserts nothing, or only the happy path, kills no mutants and is rejected — see /mutation-testing and ADR-113.

Activation

  • When generating tests for new code
  • When improving test coverage
  • When migrating tests between frameworks
  • When applying TDD patterns
  • When generating edge case tests

Quick Start

bash
# Generate unit tests for a file
aqe test generate --file src/services/UserService.ts --framework jest

# Generate tests with coverage target
aqe test generate --scope src/api/ --coverage 90 --type unit

# Generate integration tests
aqe test generate --file src/controllers/AuthController.ts --type integration

# Generate from patterns
aqe test generate --pattern repository --target src/repositories/

Agent Workflow

typescript
// Spawn test generation agents — durable-first
Task("Generate durable-first tests", `
  Analyze src/services/PaymentService.ts and generate Jest tests in tier order.
  1. DURABLE (write these first, >=1 per public method):
     - Invariants ("a refund never makes a balance negative")
     - Contract/schema checks on inputs and outputs crossing the boundary
     - Property-based tests (fast-check) over input ranges, not single examples
  2. EPHEMERAL (the red-green loop; tag '// @tier ephemeral'):
     - Specific happy-path examples and error paths
     - Mock external dependencies ONLY — never let a mock-call assertion be the
       only test for a method
  Apply the language-swap check: if a Python rewrite of PaymentService would break
  the test, move it up to the durable tier.
  Output to tests/unit/services/PaymentService.test.ts
`, "qe-test-architect")

// Property + contract generation (first-class, not opt-in)
Task("Generate property and contract tests", `
  For src/repositories/, derive:
  - Properties: round-trip (write→read returns same), idempotence, ordering invariants
  - Contracts: the repository interface schema, enforced on every CRUD result
  These survive a storage-engine swap; example-based CRUD tests do not.
`, "qe-property-tester")

Test Generation Strategies

1. Code Analysis Based

typescript
await testGenerator.analyzeAndGenerate({
  source: 'src/services/OrderService.ts',
  analysis: {
    methods: true,
    branches: true,
    dependencies: true,
    errorPaths: true
  },
  output: {
    framework: 'jest',
    style: 'describe-it',
    assertions: 'expect'
  }
});

2. Pattern-Based Generation

typescript
await testGenerator.applyPattern({
  pattern: 'service-layer',
  targets: ['src/services/*.ts'],
  customizations: {
    mockStrategy: 'jest.mock',
    asyncHandling: 'async-await',
    errorAssertion: 'toThrow'
  }
});

3. Coverage-Driven Generation

typescript
await testGenerator.fillCoverageGaps({
  coverageReport: 'coverage/lcov.info',
  targetCoverage: 90,
  prioritize: ['uncovered-branches', 'error-paths'],
  maxTests: 50
});

Framework Support

FrameworkUnitIntegrationE2EMocking
Jest⚠️jest.mock
Vitest⚠️vi.mock
Mochasinon
Pytestpytest-mock
JUnitMockito

Test Quality Checks

yaml
quality_checks:
  durability:                  # the primary check (ADR-113)
    durable_assertions_per_target: 1   # >=1 invariant/contract/property each
    language_swap_safe: true           # would survive a reimplementation
    tier_tags_present: true            # every test tagged durable|ephemeral|live

  fault_detection:             # do the tests actually catch bugs?
    mutation_score_min: 0.6            # kill rate against seeded mutants
    no_assertionless_tests: true       # reject tests that kill 0 mutants

  assertions:
    minimum_per_test: 1
    meaningful: true

  isolation:
    no_shared_state: true
    proper_setup_teardown: true

  naming:
    descriptive: true
    follows_convention: true

  coverage:                    # necessary but NOT sufficient — see fault_detection
    branches: 80
    statements: 85

Skill Composition

  • After generating tests → Run /mutation-testing to verify test quality
  • Before generating → Use /test-automation-strategy to choose framework and patterns
  • Related/qe-coverage-analysis to find where tests are needed most

Gotchas

  • Agent truncates output on files >3000 lines — scope generation to individual modules, not entire directories
  • Components that pass unit tests individually may have zero integration wiring — always generate at least one integration test per module boundary
  • When generating tests for a new codebase, check which framework is installed (jest vs vitest vs mocha) — they have different mock APIs and Claude will use the wrong one
  • Completion theater: agent may claim "comprehensive tests generated" but leave stubs or hardcoded values — always run the generated tests before accepting
  • Fleet must be initialized before using QE agents: run aqe health to diagnose, or aqe init to re-initialize if you get "Fleet not initialized"

Coordination

Primary Agents: qe-test-generator, qe-pattern-matcher, qe-test-architect Coordinator: qe-test-generation-coordinator Related Skills: qe-coverage-analysis, qe-test-execution

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qe Test Generation AI skill do?

Generates durable-first tests — invariants, contracts, and property-based tests at boundaries that survive a reimplementation — plus unit, integration, and e2e coverage. Use when creating tests for new or changed code, filling coverage gaps, or migrating test suites between Jest, Vitest, and Playwright.

Why use Qe Test Generation on TypingMind?

Because you install it once and use it with any model. Qe Test Generation 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 Qe Test Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/qe-test-generation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qe Test Generation?

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 Qe Test Generation?

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

Is the Qe Test Generation AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. 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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