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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
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Provides expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".
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Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.
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Build responsive layouts with CSS Grid, Flexbox, and container queries. Use when creating responsive designs, fixing layout issues, or building mobile-first layouts.
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CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.
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Generate 30+ LinkedIn posts that attract your target prospects. Creates industry insights, thought leadership, engagement prompts, and comment strategies. Use when building personal brand to attract inbound leads through social selling.
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Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.
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Writes conversion-focused landing page copy - headlines, subheads, value propositions, feature and benefit blocks, social proof, FAQs, and CTA button text - using PAS, AIDA, or StoryBrand. Use when the user wants words for a landing page, sales page, product or pricing page, waitlist page, homepage hero, or ad-to-page flow, including rewriting weak copy, generating headline or CTA variants for A/B tests, or turning a product description into page sections. Use landing-page-optimizer instead for auditing an existing page's performance, and a frontend skill for building the page itself.
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Use before building anything from a vague request — a feature, an app, an agent, an automation, a page. Trigger when the user says "build me X" without specifics, when a brief could be read two ways, or when the user asks to be challenged, interrogated, or pushed on requirements before code gets written.
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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.
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Create design systems with tokens, components, and documentation. Use when building design systems, creating component libraries, or establishing design tokens.
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Audit a website or UI and de-AI it — find the patterns that make it look template-generated (in both design and copy) and return specific, prioritized fixes that push it toward editorial, premium, human-crafted. Reviews layout, color, type, spacing, motion, accessibility, AND the words. Use after building or generating any frontend, or when a site looks "fine but generic" and you can't say why.
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Security review checklist for construction software systems. Use when building integrations, APIs, data pipelines, or dashboards for construction projects.
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Calculate CO2 emissions and carbon footprint from BIM model data. Analyze embodied carbon by material, element, and building system.
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AI-powered construction defect detection using computer vision. Identify cracks, spalling, corrosion, and other defects in concrete, steel, and building components from images and video.
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Building energy simulation and analysis for construction. Calculate heating/cooling loads, evaluate envelope performance, optimize HVAC sizing, and ensure energy code compliance.
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Browse and search the OpenConstructionERP cost database: classification tree, SQL and semantic search, autocomplete, certainty badges, and the resource catalog. Use when a user wants to explore cost data without building a BOQ yet.
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MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
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Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.
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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.
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Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
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Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
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Design tools, scripts, and CLIs that an agent will call, so the interface teaches its own use instead of a wall of prose and examples. Use when building an MCP server or tool definition, writing an agent-facing script, or when an agent keeps misusing a tool it already has.
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。