qa-ai-assisted

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.

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.

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.

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.

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.

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](https://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.