Testland

Build a coverage matrix for a networked-game state machine that exercises connect / authority-handoff / disconnect / reconnect / host-migration paths across Unity Netcode for GameObjects, Unreal Engine replication, and Mirror Networking. Workflow: enumerate the engine's connection states + ownership states + replicated-property update rules, cross them against latency / loss / out-of-order packet injection, encode each combination as a test fixture, and emit a go / no-go gate. Use before submitting a multiplayer title to platform cert - Microsoft's cert guide lists 'Multiplayer does not work as expected' as one of the most common Hold reasons, and Xbox XR-067 (MPSD session state) is failed by uncovered state-machine paths.

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Reference catalog of game-QA test categories and the four platform-holder certification regimes a multi-platform title submits to before release. Defines the six canonical test categories (functional / compliance / compatibility / performance / localization / accessibility) plus the multiplayer and content-rating cross-axes; documents the submission workflow, severity vocabulary, test-bench configurations, and known SLAs for Microsoft Xbox Requirements (XR), Sony TRC (gated PlayStation DevNet), Nintendo Lotcheck (gated Nintendo Developer Portal), and Steam Direct review; and includes the building-a-cert-checklist workflow that maps a target platform's requirement items to the six categories. Cites public sources inline; cites gated NDA portals by stable ID per PLUGIN_AUTHORING.md Step 4 fallback. Use when planning a game test pass or cert calendar, mapping internal QA findings to the platform's vocabulary, sequencing submissions across platforms, or emitting a pre-submission checklist.

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Author and run the Unity game-engine Test Framework (`com.unity.test-framework`, currently v1.8). Distinct from the ThrowTheSwitch Unity C testing library at throwtheswitch.org/unity - the two tools share only a name. Covers package install via Package Manager, the EditMode vs PlayMode split, the [Test] / [UnityTest] / [SetUp] / [TearDown] / [UnityPlatform] attributes, assembly-definition setup (Editor folder vs asmdef with `includePlatforms` / `optionalUnityReferences: [TestAssemblies]`), Test Runner window, command-line batch invocation with `-runTests` / `-testPlatform` / `-testResults` / `-testFilter` / `-testCategory`, NUnit 3.5 assertion API, and CI integration. Use when the unit under test is C# Unity code that needs to exercise the Unity runtime or editor.

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Author and run Unreal Engine's Automation Test Framework - Epic's C++ test framework for UE 4.x / 5.x, documented at dev.epicgames.com/documentation/en-us/unreal-engine. Covers the five test categories Epic defines (Unit / Feature / Smoke / Content Stress / Screenshot Comparison), the IMPLEMENT_SIMPLE_AUTOMATION_TEST and IMPLEMENT_COMPLEX_AUTOMATION_TEST macros, the BDD-style Automation Spec API (DEFINE_SPEC / BEGIN_DEFINE_SPEC / Describe / It / BeforeEach / LatentIt / xIt), latent commands (ADD_LATENT_AUTOMATION_COMMAND), the Automation Driver for UI input simulation (IAutomationDriverModule::Get().CreateDriver(), By::Id / By::Path locators), running via Session Frontend (Window > Test Automation) and command line (-ExecCmds="Automation RunTests …"), and CI integration. Use when the unit under test is C++ Unreal code that needs the UE runtime, editor, or UMG UI surface.

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Wraps Apollo Server testing patterns: `server.executeOperation()` (in-process, no HTTP), `supertest` against an ephemeral-port HTTP server (port 0), context injection via the `contextValue` second-argument, and assertion patterns for response shape + errors. Includes the production-config gates testable through this skill - introspection-disabled, persisted-query mode, hideSchemaDetailsFromClientErrors. Use when writing tests for an Apollo Server v4+ GraphQL service.

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The GraphQL attack-surface / hardening skill: crafts over-limit depth and complexity queries then asserts rejection before execution, verifying that graphql-depth-limit, graphql-cost-analysis, and graphql-armor (max-depth / cost-limit / max-tokens plugins) are actually enforced and not just configured; carries the introspection attack-surface catalog (what __schema exposes, per-framework disable controls, hideSchemaDetailsFromClientErrors) in references/introspection.md and the persisted-query allowlisting strategies (Apollo APQ protocol, auto-register vs strict-allowlist vs hybrid modes) in references/persisted-queries.md. Use when auditing a GraphQL service for DoS or schema-disclosure exposure, hardening a production deployment, or adding tests that prove the limits in CI.

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Detects and fixes the GraphQL N+1 pattern: scans a repo, PR diff, or schema type for list-returning resolvers (grep-driven detection workflow), traces the resolver tree to locate the fan-out (one parent query returns N rows, then a child field resolver fires once per row), classifies every child field resolver as safe or N+1 risk, and applies one of three fixes: per-request DataLoader batching, eager projection in the parent resolver, or selection-set-aware prefetch. Use when reviewing a PR that adds or changes a list-returning resolver, when a connection-pool exhaustion or slow-query alert traces back to GraphQL traffic, or when a resolver trace shows a child field resolved once per parent row.

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Authors GraphQL subscription resolver test suites over graphql-ws (WebSocket) and graphql-sse (Server-Sent Events) transports: subscribe to event streams via the async-iterator API, assert emitted data shape and sequence, verify connection lifecycle and protocol close codes, and validate auth-on-connect (connectionParams / authenticate callback) plus resolver-level pubsub trigger logic. Use for real-time subscription operations; not for queries or mutations - for those use apollo-server-tests or graphql-yoga-tests (Mercurius in its references).

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Tests a GraphQL Yoga server (the-guild.dev runtime) with `yoga.fetch()` for in-process, no-network request simulation of queries and mutations, `@graphql-tools/executor-http` for subscription and incremental-delivery (streaming) tests, auth-header pass-through, and production-config gates for disabled introspection and persisted operations; also carries the Mercurius (Fastify GraphQL plugin) in-process `app.inject()` testing patterns in references/mercurius.md. Use to test a GraphQL Yoga or Mercurius server, write query, mutation, or subscription tests, or check production plugin config; for other runtimes use apollo-server-tests or hasura-tests instead, not this skill.

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Wraps Hasura GraphQL Engine testing patterns: docker-compose test instance, the metadata API for declarative schema/permission setup, x-hasura-role and x-hasura-user-id session headers for role-based permission tests, the v1/graphql endpoint via curl/HTTPie/native HTTP clients, and the role-by-table-by-operation permission-matrix pattern. Use for a metadata-driven Hasura engine where row-level permissions dominate; for a code-first server runtime harness use graphql-yoga-tests (Mercurius in its references) or apollo-server-tests instead, not this skill.

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Wraps the buf CLI for protobuf PR gating: `buf build` (compile .proto), `buf lint` (STANDARD rules: snake_case fields, Service suffix), `buf breaking --against {ref}` (detect wire/codegen breakage vs a git/BSR baseline), and `buf format`. Use as the CI proto-lint + breaking-change gate, or to debug a breaking failure by rule ID (e.g. FIELD_NO_DELETE_UNLESS_NUMBER_RESERVED) and pick the FILE/PACKAGE/WIRE_JSON/WIRE ruleset per consumer. This is the detection TOOL that enforces the rules and carries the catalog of what is breaking and why (field-number reservation, wire-safe vs wire-incompatible changes, oneof/map constraints, the four buf categories) in references/versioning-strategy.md; for cross-service schema contract testing use protobuf-compat-checking - not this.

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Wraps ghz, the gRPC load testing tool, for throughput and latency benchmarking. Covers test invocation (--proto + --call + host:port; or --protoset for compiled descriptors), load parameters (-n total requests, -c concurrency, -r RPS rate limit, -z duration), output formats (json/csv/html/influx-summary for CI consumption), the metrics reported (RPS achieved, latency p50/p95/p99, status-code distribution, errors), and CI integration patterns for regression gating. Use when benchmarking a gRPC service's throughput or detecting latency regressions in CI.

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Wraps gRPC server-mocking patterns for client-side tests: Go bufconn (in-memory net.Listener via google.golang.org/grpc/test/bufconn) + mockgen-generated interface mocks, Python pytest-grpc fixtures + unittest.mock patching of stubs, JVM grpc-mock library / in-process gRPC server (InProcessServerBuilder), Node @grpc/grpc-js fake server with NewServer-on-port-0. Also carries the interceptor-layer test patterns (Go / Java / grpc-js auth, retry, logging, error-mapping, chained ordering via a spy handler) in references/interceptors.md. Use when writing client-side tests that need a controllable gRPC server response (success cases, error cases, timeouts, single-response error injection) without spinning up a real backend, or when testing a gRPC interceptor. For multi-message streaming-sequence tests (server-streaming, bidi), use grpc-streaming-test-author instead. Distinct from grpcurl-cli (ad-hoc CLI invocation against a real server) and ghz-load (perf against a real server).

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The single gRPC-streaming test home: builds streaming-RPC test suites from a proto definition. Classifies each RPC by pattern (unary, server-streaming, client-streaming, bidi), then emits the required categories per pattern - ordering preservation, completion semantics (server close after stream end, client half-close), cancellation, deadline handling, partial-stream failure. Produces skeletons for Go (bufconn + Send/Recv), Python (iterators), JVM (StreamObserver), Node (call.write/end); carries the 17-code gRPC status catalog (retry semantics per AIP-194, grpc-gateway HTTP mapping) in references/status-codes.md and the wire-level / live-server streaming patterns (deadline propagation, server-side cancellation, metadata, ghz load) in references/wire-level-testing.md. Use when adding tests for a new or existing streaming RPC, auditing a suite for uncovered categories, or asserting status-code behavior. Different test surface from grpc-mock (the in-process harness itself).

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Wraps grpcurl, the curl-equivalent CLI for gRPC. Covers descriptor sources (server reflection default, --import-path + --proto for proto files, --protoset for compiled descriptor sets), service discovery (`list`, `describe`), invoking unary RPCs (`-d '{...}'`, `-d @file.json`, `-d @` for stdin), streaming RPCs (newline-delimited JSON via stdin), TLS configuration (--cacert, --cert, --key, --insecure, --plaintext), header injection (-H 'Authorization: Bearer ...'), and exit codes. Use for ad-hoc gRPC debugging, smoke testing, scriptable PR-time gates, and CLI-based interaction with reflective gRPC services.

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Configures Checkov for IaC security scanning across Terraform, CloudFormation, Kubernetes, Helm, ARM, Serverless, AWS CDK - `pip install checkov`, custom Python checks, SARIF / JUnit output, and `--baseline` gating so CI fails only on new findings in legacy code. Use for the broadest built-in rule set with Python custom checks; for a consolidated new-project scanner (and the tfsec successor) use trivy-config.

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Configures helm-unittest for Helm chart unit testing - installs the `helm-unittest` plugin, authors `tests/*.yaml` per template, asserts on rendered manifests (`isKind`, `equal`, `matchRegex`, snapshots), plus `helm lint` and `helm template` render testing. Use to verify a chart's template logic and rendered shape; this is unit-level correctness testing, not security scanning - to enforce policy on rendered manifests use policy-as-code-runner, and to scan charts for misconfigurations use checkov-policy or trivy-config.

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Runs Trivy's misconfiguration scanner (`trivy config`) against IaC directories to detect security issues across Terraform, CloudFormation, Kubernetes manifests, Helm charts, Dockerfiles, and Azure ARM templates - installs Trivy, scans with severity gating via `--exit-code`, suppresses findings via `.trivyignore` / `.trivyignore.yaml` or inline annotations, extends built-in checks with custom Rego policies, and emits SARIF for GitHub Code Scanning. Trivy is the tfsec successor - the forward path from tfsec per Aqua Security's own migration guidance - and the legacy tfsec workflow (install, custom YAML rules, ignore annotations, migration steps) is kept in references/tfsec-legacy.md. Use when adopting a consolidated IaC scanner for new projects, migrating away from tfsec (or still operating a Terraform-only tfsec stack), or scanning mixed IaC stacks with a single tool.

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Authors and runs DeepEval - pytest-native LLM eval framework with `LLMTestCase` (input + actual_output + expected_output + retrieval_context) and ~11 built-in metrics (G-Eval, Answer-Relevancy, Faithfulness, Contextual-Recall / Precision / Relevancy, Hallucination, Bias, Toxicity, Summarization, JSON-Correctness); runs via `deepeval test run {file.py}` with `assert_test()` per test or `evaluate()` for batch; integrates Confident-AI dashboard. Use when the user prefers pytest workflow, works with RAG and needs faithfulness/contextual metrics out-of-the-box, or wants a managed dashboard.

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Authors and runs Giskard LLM scans - adversarial test-case generation for LLM applications via `giskard.scan(model)` covering 7 vulnerability categories (hallucination, harmful_content, prompt_injection, sensitive_information_disclosure, stereotypes, robustness, basic_sycophancy); wraps any callable model behind `giskard.Model(model_predict, model_type="text_generation", ...)`; emits HTML report. Use when the user needs adversarial / red-team coverage on top of functional eval suites.

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Wires Langfuse tracing into LLM apps for production observability, monitoring, telemetry, and offline eval - instruments via `@observe` (Python) / `startActiveObservation` (TS) decorators that auto-capture inputs / outputs / timings / errors per generation; exposes `langfuse.update_current_span()` for metadata + cost / latency annotation; supports trace-bound scoring for eval datasets and prompt-as-code management. Use when the user needs to monitor, log, trace, or debug LLM API calls in production beyond pre-deploy eval, wants to add LLM observability tooling to an existing app, or wants to ship traces from production to an eval dataset for offline regression testing.

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Audits an existing LLM evaluation suite for eight methodology errors that make its numbers untrustworthy: too few cases per capability, single-provider lock-in, exact-match assertions on open-ended output, no semantic-similarity check on paraphrase-tolerant output, no baseline comparison in CI, no cost or latency ceiling, unpinned model identifiers, and no adversarial coverage. Supplies harness-neutral detection cues, the reason each error invalidates the result, a concrete fix, a Critical/Warning/Info severity scheme, and a findings-table output shape. Covers validating an LLM judge against human labels before its verdicts count as evidence. Use when an eval suite already exists and its pass rate is about to gate a release, a model swap, or a prompt change, and nobody has audited how the suite itself was built.

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Builds a versioned golden-dataset LLM regression suite for tracking quality across model upgrades: structures a versioned JSONL/CSV golden dataset, configures deterministic eval runs (temperature 0, seed), wires assertion layers (exact, semantic similarity, LLM-as-judge, rubric), enforces a pass-rate threshold with diff reporting vs the baseline model, and gates CI on regression. Use when upgrading an LLM provider model and needing a repeatable before/after quality gate, or when a prompt regression suite must track output quality across model versions over time.

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Authors and runs Promptfoo evals for LLM prompts and RAG pipelines - wires `promptfooconfig.yaml` providers + prompts + tests + assertions (deterministic `equals` / `contains` / `is-json` / `regex`, semantic `similar`, model-graded `llm-rubric` / `factuality` / `g-eval`, performance `latency` / `cost`, custom `javascript` / `python`), runs `npx promptfoo eval`, views HTML report via `promptfoo view`, and integrates CI for regression gating. Use when the user runs Promptfoo, asks about prompt regression suites, or needs an eval-driven workflow for LLM-backed features.

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