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概要と使いどころ
Provides patterns for unit testing service layer with Mockito. Creates isolated tests that mock repository calls, verify method invocations, test exception scenarios, and stub external API responses. Use when testing service behaviors and business logic without database or external services.
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Provides patterns for unit testing Spring `@Scheduled` and `@Async` methods using JUnit 5, CompletableFuture, Awaitility, and Mockito. Covers mocking task execution and timing, verifying execution counts, testing cron expressions, validating retry behavior, and simulating thread pool behavior. Use when testing background tasks, cron jobs, periodic execution, scheduled tasks, or thread pool behavior.
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 and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven 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 chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
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 comprehensive testing patterns for Spring Boot applications covering unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when writing tests, @Test methods, @MockBean mocks, or implementing test suites for 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。
Add or revise source-level JSDoc for Shift APIs. Use this skill before writing or editing documentation comments for exported classes, methods, constructors, domain data structures, render frames, reactive state, or any API where caller intent, side effects, lifetime, ownership, or nullability are easy to misunderstand.
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 after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation
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Use when receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes
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。
Calculate construction costs using resource-based method. Estimate project costs from work items, physical resource norms, and current prices.
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。
Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.
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。
Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods
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。
Analyze schedule delays, identify causes, and calculate time impacts using delay analysis methods.
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。
Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.
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。
Base/variable split recommendations, accelerators, decelerators, quota retirement methods, SPIFs, cost of sales tracking.
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。
TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.
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。
Generates structured OKR plans (Objectives and Key Results) for teams and companies following Google/Intel methodology. Takes company goals, team function, quarter, and current metrics to produce a comprehensive okr-plan.md with objectives, key results, scoring criteria, alignment mapping, tracking cadence, and retrospective templates.
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。
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
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。
Take any claim and design + execute a complete research protocol to test it. Apply scientific method automatically: design study, gather data, run analysis, provide verdict with confidence level.
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 before writing code against a library, SDK, API, or framework whose current version you are not certain of — especially fast-moving ones. Trigger when an import fails, a method does not exist, a config key is rejected, a deprecation warning appears, or the user says the code was written against an old version.
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。
Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
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 applying a published cohort study's methodology to a different database. Extracts the design from the source paper, maps variables to the target database with a harmonization table, generates the analysis code and reports every forced deviation.
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Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
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 one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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。
Generate diverse solution candidates with category-spanning ideation methods and rotation. Use when stuck on a design or fighting repetitive LLM output.
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。