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Found 81 Skills
Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.
Evaluate UI/flows from cognitive load, error prevention, and accessibility perspectives. Apply when reviewing UX, discussing user confusion, high drop-off, or form usability issues.
Use when the user wants to push past conventional workflow limits with advanced performance techniques like parallel orchestration, streaming pipelines, or adaptive routing.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Use when the agent needs access to information beyond its training data — knowledge sources, RAG pipelines, or grounding data.
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.
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.**
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.
Design thinking maestro for human-centered design processes. Use when the user asks to talk to Maya or requests the Design Thinking Maestro.
UiPath IXP (Document Understanding) via `uip ixp` — create projects (with autopilot taxonomy suggestion, an imported taxonomy file, or empty), upload/download/delete documents, author the taxonomy (field groups, fields, data types, per-field and overall extraction instructions), configure the extraction model and pre-processing, review/confirm/unconfirm predictions, mark fields missing, pull metrics and model versions, publish/tag/roll back model versions, deploy a trained version to an Orchestrator folder and move an existing deployment to another version (`deployments create`/`upgrade`/`list`). DO NOT TRIGGER during .flow / Maestro Flow work — discovering or listing IxP / document-extraction models, extractors, or nodes available to Maestro Flow, and adding or wiring an IxP node, belong to uipath-maestro-flow even when they sound like IXP model management.
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.
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.