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Found 56 Skills
Use when you need maximum precision on a critical task — production deployments, security-sensitive code, financial calculations, or any work where mistakes are unacceptable.
Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.
Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.
Use this skill whenever the user is working with AdonisJS v7 backend framework code: controllers, routes, middleware, services, VineJS validators, Transformers, Bouncer policies, events, listeners, mail, cache, queue, exceptions, Ace commands, request/response/session handling, or backend architecture and review. Trigger for "create a controller", "add validation", "create a service", "add a policy", "wire routes", "handle an exception", or AdonisJS backend review/debugging. For Lucid ORM, migrations, schema generation, models, relationships, query builders, transactions, factories, or seeders, use the lucid skill alongside or instead of this one. For Japa tests, use the japa skill. For Inertia frontend patterns, use inertia-react or inertia-vue alongside this one.
Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.
Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.
Use when working with AdonisJS Lucid ORM and SQL layer: database configuration, migrations, schema generation, schema classes, models, CRUD operations, model query builder, query scopes, hooks, serialization, relationships, transactions, pagination, debugging, validation rules, model factories, seeders, or database query builders. Trigger for tasks involving @adonisjs/lucid, database/schema.ts, app/models, database/migrations, database/factories, database/seeders, db service queries, Lucid relationships, or model behavior.
Mobile app testing strategy and execution for iOS and Android (native + cross-platform): choose automation frameworks, define device matrix, control flakes, validate performance/reliability/accessibility, and set CI + release gates. Use when you need a mobile QA plan, device lab/CI setup, or guidance on XCUITest/Espresso/Appium/Detox/Maestro/Flutter testing.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Inspects, filters, and maps z-schema validation errors for application use. Use when the user needs to handle validation errors, walk nested inner errors from anyOf/oneOf/not combinators, map error codes to user-friendly messages, filter errors with includeErrors or excludeErrors, build form-field error mappers, use reportPathAsArray, interpret SchemaErrorDetail fields like code/path/keyword/inner, or debug why validation failed.
Schema lifecycle management for Basic Memory: discover unschemaed notes, infer schemas, create and edit schema definitions, validate notes, and detect drift. Use when working with structured note types (Task, Person, Meeting, etc.) to maintain consistency across the knowledge graph.
Design thinking maestro for human-centered design processes. Use when the user asks to talk to Maya or requests the Design Thinking Maestro.