qa-ai-assisted
AI-assisted test generation + curation: 3 skills (ai-spec-coverage-mapper, ai-test-generator, model-based-test-graph-author) and 3 agents (ai-test-curator, ai-test-shallow-coverage-critic, mbt-suite-builder).
Install this plugin
/plugin install qa-ai-assisted@testland-qaPart of role bundle: qa-role-ai
qa-ai-assisted
AI-assisted test generation + curation. AI-generated tests from natural-language specs, coverage gap mapping, two adversarial reviewers for AI-generated tests (one catches hallucinations / weak assertions / redundancy, the other catches shallow happy-path-only input-domain coverage), plus a model-based-test graph authoring skill that produces structured input AI test generators benefit from.
Components
| Type | Name | Description |
|---|---|---|
| Skill | ai-test-generator | Build-an-X workflow that uses an LLM to generate tests from natural-language specs (acceptance criteria, user stories) - outputs tests with confidence scoring per case (LLM's own self-assessment + heuristics: assertion-quality, naming, completeness), batches uncertain cases for human review, integrates with the team's existing test framework. Critical: AI-generated tests are unreliable without curation; pairs with ai-test-curator (the adversarial reviewer). Use when a team has many AC to convert and wants AI-augmentation, not AI-replacement. |
| Skill | ai-spec-coverage-mapper | Build-an-X workflow that uses an LLM to map existing tests to spec sections - given a spec doc + the test suite, the LLM identifies which tests cover which sections, surfaces uncovered sections (gap), and recommends specific tests to add. Output is a coverage matrix per spec ID. Use as a follow-up to ai-test-generator (which generates tests for new ACs) - this maps the existing landscape and finds what's missing. |
| Skill | model-based-test-graph-author | Build-an-X workflow for model-based testing (MBT) per the canonical definition - authors a state-machine model of the SUT (states + transitions + guards + actions), validates the model is connected and complete, and feeds the model to a test generator (manual / AI / dedicated MBT tool) that produces test paths covering each transition. Per Wikipedia (opens in new window): MBT "leverages model-based design for designing and possibly executing tests." Use when a complex stateful flow (checkout, onboarding, multi-step wizard) needs systematic coverage that ad-hoc tests miss. |
| Skill | input-domain-coverage-audit | Audits whether a test file spans the input space: equivalence partitioning, boundary values, and negative paths, with a PASS / SHALLOW / N-A verdict per axis per entry point. |
| Agent | ai-test-curator | Adversarial reviewer for AI-generated tests - reads the LLM's output and flags hallucinated APIs (functions / classes / imports the LLM invented), weak assertions (.toBeTruthy() style), redundancy with existing tests, missing setup/teardown, and naming patterns the LLM defaults to. Refuses to mark generated tests "ready" if any high-confidence issue remains. Use as the required downstream gate for ai-test-generator - never merge AI-generated tests without this curator's approval. |
| Agent | ai-test-shallow-coverage-critic | Adversarial reviewer that flags tests covering only the happy path - same valid input class, same nominal flow, no boundaries, no error branches, no negative cases. Distinct from ai-test-curator (which catches hallucinated APIs and weak assertions) and from assertion-quality-reviewer (which catches vague matchers): this agent targets input-domain coverage using the ISTQB equivalence-partitioning and boundary-value-analysis techniques. Refuses to clear a test file unless the suite covers at least one boundary case and at least one error/negative case per public entry point. Use as the required downstream gate after any AI-assisted test generation, including ai-test-generator, Copilot-suggested tests, and Cursor-authored tests. |
| Agent | mbt-suite-builder | Single entry point for the model-based-testing pipeline: model graph to covering tests to suite. |
Install
/plugin marketplace add testland/qa
/plugin install qa-ai-assisted@testland-qaSkills
ai-spec-coverage-mapper
Build-an-X workflow that uses an LLM to map existing tests to spec sections - given a spec doc + the test suite, the LLM identifies which tests cover which sections, surfaces uncovered sections (gap), and recommends specific tests to add. Output is a coverage matrix per spec ID. Scope is mapping tests that already exist and naming the gaps, not authoring tests for new acceptance criteria. Use when a spec doc and a test suite both exist but nobody can say which requirements are actually covered - before a release sign-off, an audit, or a decision about where to spend the next round of test effort.
ai-test-generator
Generates tests from natural-language specs (acceptance criteria, user stories, requirements) using an LLM, with confidence scoring per test case (LLM self-assessment plus heuristics: assertion quality, naming, completeness), batching uncertain cases for human review, and integration with the team's existing test framework. Use when the user asks to generate unit tests from acceptance criteria, convert user stories to test cases, automate test creation from requirements, or augment a spec-driven test suite with AI-generated stubs that are then curated before merge.
input-domain-coverage-audit
Audits a test file's input-domain coverage per entry point across three axes: equivalence partitioning (clustering the literal values the tests actually pass, to infer which partitions are exercised), boundary value analysis (recorded n/a when the entry point declares no bound), and error/negative-path coverage (classifying every matcher as positive or negative and computing the negative-assertion ratio). Emits a PASS / SHALLOW / N/A verdict per axis per entry point, with the evidence that produced it. Owns whether the test data spans the input space, not whether an individual assertion is specific enough: matcher specificity belongs to `test-code-conventions`. Use when a test file's cases all look alike - every argument the same shape, every response a success, no thrown-error case - and the suite needs a defensible answer on whether it exercises more than one equivalence class before it is approved.
model-based-test-graph-author
Build-an-X workflow for model-based testing (MBT) per the canonical definition - authors a state-machine model of the SUT (states + transitions + guards + actions), validates the model is connected and complete, and feeds the model to a test generator (manual / AI / dedicated MBT tool) that produces test paths covering each transition. Per Wikipedia (en.wikipedia.org/wiki/Model-based_testing): MBT "leverages model-based design for designing and possibly executing tests." Use when a complex stateful flow (checkout, onboarding, multi-step wizard) needs systematic coverage that ad-hoc tests miss.
Agents
ai-test-curator
Adversarial reviewer for AI-generated tests - reads the LLM's output and flags hallucinated APIs (functions / classes / imports the LLM invented), weak assertions (`.toBeTruthy()` style), redundancy with existing tests, missing setup/teardown, and naming patterns the LLM defaults to. Refuses to mark generated tests "ready" if any high-confidence issue remains. Use as the required downstream gate for `ai-test-generator` - never merge AI-generated tests without this curator's approval.
ai-test-shallow-coverage-critic
Adversarial reviewer that flags tests covering only the happy path - same valid input class, same nominal flow, no boundaries, no error branches, no negative cases. Distinct from `ai-test-curator` (which catches hallucinated APIs and weak assertions) and from `assertion-quality-reviewer` (which catches vague matchers): this agent targets **input-domain coverage** using the ISTQB equivalence-partitioning and boundary-value-analysis techniques. Refuses to clear a test file unless every applicable axis passes per public entry point, recording boundary analysis as not applicable where the parameter declares no ordered bound. Use as the required downstream gate after any AI-assisted test generation, including `ai-test-generator`, Copilot-suggested tests, and Cursor-authored tests.
mbt-suite-builder
Action-taking orchestrator that builds a complete model-based test suite for a stateful SUT in one pass: derives the state/transition graph via model-based-test-graph-author, feeds the validated model to ai-test-generator to produce covering test cases, and emits the assembled suite with a curation note routing to ai-test-curator before merge. Distinct from ai-test-curator (adversarial reviewer only) and model-based-test-graph-author (model authoring only) - this agent is the single entry point for the full MBT pipeline. Use when a mid/senior SDET needs to bootstrap a model-based test suite for a complex stateful flow (checkout, onboarding, multi-step wizard) and wants the graph derivation and test generation done in one coordinated run.