全部能力

Generates deterministic seed data for development and testing with factory functions, realistic fixtures, and database reset scripts. Use for "data seeding",…

Optimizes CI/CD pipeline speed by implementing effective caching for dependencies, Docker layers, build outputs, and test results. Provides before/after…

Optimizes CI pipelines for monorepos by detecting affected packages/apps and running only necessary builds and tests. Includes Turborepo/Nx strategies,…

Automates npm release workflows using changesets. Creates a changeset (default patch), fixes lint/test/typecheck/format issues, commits and pushes, watches CI…

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Defines ROI-based coverage targets with critical path identification, layer-specific targets, and explicit "don't test this" guidelines. Use for "test…

Add, update, or remove text/image/video models. Handles any provider.

Expert in web accessibility (WCAG 2.1 AA compliance) for React/Next.js applications, ensuring all projects are usable by everyone

Design comprehensive test cases using PICT (Pairwise Independent Combinatorial Testing) for any piece of requirements or code. Analyzes inputs, generates PICT…

Use when wrapping UIKit views/controllers in SwiftUI, embedding SwiftUI in UIKit, or debugging UIKit-SwiftUI interop issues. Covers UIViewRepresentable,…

Use when implementing drag and drop, copy/paste, ShareLink, or ANY content sharing between apps or views - covers Transferable protocol, TransferRepresentation…

Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.

Sample skill for testing the skill-tester validation pipeline. Demonstrates proper skill structure with scripts, references, and assets.

MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.

Creates integration tests for API endpoints with database flows, including test harness setup, fixtures, setup/teardown, database seeding, and CI-friendly…

Guide for building Apple-platform applications with Apollo iOS, the strongly-typed GraphQL client for Swift. Use this skill when: (1) adding Apollo iOS to a Swift Package Manager or Xcode project, (2) configuring `apollo-codegen-config.json` and running code generation, (3) configuring an `ApolloClient` with auth, interceptors, and caching, (4) writing queries, mutations, or subscriptions from SwiftUI views, (5) writing tests against generated operation mocks.

Use this skill whenever writing, reviewing, debugging, or refactoring TypeScript code that uses the Effect-TS library. Trigger when you see imports from `effect`, `effect/*`, or any `@effect/*` scoped package (schema, platform, sql, opentelemetry, cli, cluster, rpc, vitest). Trigger on Effect-specific constructs: Effect.gen generators, Schema.Struct/Schema.Class definitions, Layer/Context.Tag/Service patterns, Effect.pipe pipelines, Data.TaggedError/Data.Class error types, Ref/Queue/PubSub/Deferred concurrency primitives, Match module, Config providers, Scope/Exit/Cause/Runtime patterns, or any code using Effect's typed error channel (E parameter). Also trigger when the user asks about Effect patterns, migration from Promises/fp-ts/neverthrow to Effect, or how to structure an Effect application. Covers the full ecosystem: core Effect type, Schema validation, error management, concurrency (fibers, queues, semaphores, pools), streams/sinks, services and layers (DI), resource management, scheduling, observability, platform APIs, and AI integration. Do NOT trigger for React's useEffect, Redux side effects, or general English usage of "effect" unless the context clearly involves the Effect-TS library.

Set up Biome (default) or ESLint + Prettier, Vitest testing, and pre-commit hooks for any JavaScript/TypeScript project. Uses Bun as the package manager. Use…

Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad…

Systematic 4-phase debugging: (1) understand the failure by reading the complete error and reproducing it, (2) find working examples to compare against, (3) test one hypothesis at a time with a single change, (4) fix the root cause and verify with tests. Includes the Three Strikes Rule (escalate after 3 failed fix attempts) and the Iron Law (no fixes without investigation). Activate when tests fail unexpectedly, errors occur, behavior is wrong, something that worked before is now broken, or a user reports a bug. Triggers on: "debug", "why is this failing", "test failure", "unexpected error", "bug", "broken", "investigate this error", "fix this bug", "tests are failing", "weird behavior", "something broke", "track down this issue". Also activates when multiple bugs need systematic investigation (one at a time, not batch-fixed). NOT for: writing new tests from scratch (use tdd), or general code review (use code-review).