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概要と使いどころ
Expert in React Native native modules, bridging JavaScript and native code, writing custom native modules, using Turbo Modules, Fabric, JSI, autolinking, module configuration, iOS Swift/Objective-C modules, Android Kotlin/Java modules. Activates for native module, native code, bridge, turbo module, JSI, fabric, autolinking, custom native module, ios module, android module, swift, kotlin, objective-c, java native code.
日本語の概要は準備中です。原文の説明を表示しています。
Map how customers discover, evaluate, and choose a product across Google, AI chatbots (ChatGPT, Claude, Perplexity), and social platforms, using the 5-stage awareness journey. Use when the user asks about search intent, AI SEO prompts, discoverability, buyer journeys, awareness stages, or what content to create for a brand, website, or keyword list.
日本語の概要は準備中です。原文の説明を表示しています。
Provides patterns for unit testing JSON serialization/deserialization with Jackson and `@JsonTest`. Validates JSON mapping, custom serializers, date formats, and polymorphic types. Use when testing JSON serialization, validating custom serializers, or writing JSON unit tests in Spring Boot applications.
日本語の概要は準備中です。原文の説明を表示しています。
Provides patterns for unit testing Jakarta Bean Validation (JSR-380), including @Valid, @NotNull, @Min, @Max, @Email constraints with Hibernate Validator. Generates custom validator tests, constraint violation assertions, validation groups, and parameterized validation tests. Validates data integrity logic without Spring context. Use when writing validation tests, bean validation tests, or testing custom constraint validators.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores. Also use when the user mentions "what are users saying", "competitor reviews", "pain points", or "voice of customer research".
日本語の概要は準備中です。原文の説明を表示しています。
Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
日本語の概要は準備中です。原文の説明を表示しています。
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
日本語の概要は準備中です。原文の説明を表示しています。
Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.
日本語の概要は準備中です。原文の説明を表示しています。
Design and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.
日本語の概要は準備中です。原文の説明を表示しています。
Respond to Google, Yelp, and industry reviews in the owner's voice -- gracious on the 5-stars, masterful on the 1-stars. Handles the angry customer, the unfair review, the fake review, and the one that mentions a legal or health issue. Every response written for the thousand future customers reading it.
日本語の概要は準備中です。原文の説明を表示しています。
Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize job applications by tailoring resumes to job postings, generating customized cover letters, and preparing role-specific interview questions. Analyzes job descriptions to highlight relevant skills and experience. Use when users need to apply for jobs, customize resumes, or prepare for interviews.
日本語の概要は準備中です。原文の説明を表示しています。
Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.
日本語の概要は準備中です。原文の説明を表示しています。
Build a panel of your real buyer personas (from a deep scan of any tools you allow it to connect to) and have them debate any decision you bring — a marketing launch, a price increase, a new product, a positioning change, a feature cut. Returns a structured debate, the strongest objections, and a clear recommendation. Use when you want your actual customers in the room before you commit.
日本語の概要は準備中です。原文の説明を表示しています。
Writes, explains, debugs, and optimizes BI calculations - Power BI / Fabric DAX measures and calculated columns, Tableau calculated fields (FIXED/INCLUDE/EXCLUDE LOD expressions, table calculations), and Looker LookML measures, dimensions, and derived tables. Use it whenever the request involves DAX, CALCULATE, a Power BI measure, a Tableau calc or LOD, LookML, or a business metric such as YoY, YTD, rolling 12 months, running total, percent of total, distinct customers, ranking, new vs returning customers, closing balances, or basket analysis. Use it especially when someone says a measure "shows the wrong total", "is blank", "ignores my slicer/filter", "repeats the same value on every row", or "is slow" - those are filter context, context transition, LOD order-of-operations, or fanout bugs this skill is built to diagnose. Produces the formula, the context it evaluates in, and a hand-checkable test with expected numbers.
日本語の概要は準備中です。原文の説明を表示しています。
Create detailed buyer personas and Ideal Customer Profiles (ICP) for B2B and B2C marketing. Generates comprehensive profiles with demographics, psychographics, pain points, goals, objections, and messaging strategies. Use when defining target audience, creating ICP, or developing customer profiles.
日本語の概要は準備中です。原文の説明を表示しています。
Fix "Fastly error: unknown domain" 500 errors when custom domains return "Domain Not Found" despite being created with domain-v1 CLI. Use when: (1) fastly domain-v1 create succeeds but domain returns 500, (2) Domain shows activated:false in API response, (3) Classic domain API returns "deprecated" error, (4) Custom domain works on edgecompute.app but not on your domain. Covers Fastly Compute domain setup post-September 2025.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes a repository and its test suite to generate customized mutation operators tailored to the project. Use this skill when setting up mutation testing, improving test quality, or generating project-specific mutants. Considers language constructs, business logic patterns, API calls, data types, and code complexity to produce effective mutation operators that maximize test sensitivity while avoiding trivial or equivalent mutations. Generates prioritized operator sets (high/medium/low value) with impact summaries. Triggers when users ask to generate mutation operators, customize mutation testing, analyze code for mutation opportunities, or create project-specific mutants.
日本語の概要は準備中です。原文の説明を表示しています。
ACTIVATE when drafting all-hands speeches, investor updates, quarterly letters to stakeholders, press statements, CEO announcements, crisis communications, customer updates, or any mission-critical communication to employees, investors, customers, or media. Executive communications require persuasion, clarity, emotional intelligence, and alignment with company values and strategy.
日本語の概要は準備中です。原文の説明を表示しています。
Audit Apple Ads measurement, AdServices and AdAttributionKit, campaign and keyword structure, Search Match, App Store placements, custom product pages, bidding, budgets, MMP reconciliation, and policy. Use for Apple Ads, Apple Search Ads, App Store ads, Search Match, custom product pages, AdServices, or Apple app-install campaigns.
日本語の概要は準備中です。原文の説明を表示しています。
A clean, calendar-software-first interface anchored on white canvas with black primary CTAs and custom Cal Sans display typography. The system reads as friendly modern SaaS — generous whitespace, soft-rounded cards (~12px), product UI fragments shown directly inside cards, and a dark navy footer that visually closes long-scroll pages. Brand voltage comes from the Cal Sans display headline (a custom geometric face) and from product UI artifacts shown in-card rather than from accent colors.
日本語の概要は準備中です。原文の説明を表示しています。
Use when user asks to clone a voice, train a custom voice model, or synthesize speech with a cloned voice. iFlytek Voice Clone tts(声音复刻) — train a custom voice model from audio samples and synthesize speech with the cloned voice. Supports the full workflow: get training text → create task → upload audio → submit training → poll results → synthesize with cloned voice. Pure Python stdlib, no pip dependencies.
日本語の概要は準備中です。原文の説明を表示しています。
Facilitate a structured conversation to customize how the review molecule works -- atom loading rules, severity classification, report format, scope rules, insight capture, and health logging. Produces a formal review-standards.md document that the review molecule will use as its process configuration. Use when the user says 'customize review', 'configure review', 'review preferences', 'review settings', 'change review process', or 'set up review'.
日本語の概要は準備中です。原文の説明を表示しています。
CustomShader authoring — vertexShaderText and fragmentShaderText against VertexInput, FragmentInput, FeatureIds, Metadata, czm_modelMaterial. Use when reading EXT_mesh_features or EXT_structural_metadata property textures/tables, vertex displacement, or shading VoxelPrimitive.
日本語の概要は準備中です。原文の説明を表示しています。