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概要と使いどころ
Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java 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。
When the user needs to design or evaluate system architecture — service boundaries, data models, API contracts, infrastructure topology, database selection, or dependency analysis. Also activate for "design the system", "how should I architect this", "monolith vs microservices", or architecture decision records.
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。
Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model performance.
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 data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
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。
Predict project completion dates using ML models. Forecast schedule delays based on current progress, historical patterns, and risk factors.
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。
Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.
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 4D construction simulations by linking BIM models with project schedules.
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。
Push Excel data back to BIM models. Update parameters, properties, and attributes from structured spreadsheets.
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。
Automated cost estimation from BIM models using DDC CWICR database (8 national bases, 78,228 positions). AI classification + vector search for accurate 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。
Analyze construction site photos to track progress, detect safety issues, and compare against BIM models using computer vision.
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。
Extract quantities from IFC/Revit models for quantity takeoff. Uses DDC converters to get element counts, areas, volumes, lengths with grouping and reporting.
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。
CAD/BIM takeoff in OpenConstructionERP: upload Revit/IFC/DWG/DGN models, browse elements, bulk-link them to BOQ lines, measure DWG drawings and PDF plans, and push quantities into the estimate. Use for any model-based quantification workflow.
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。
Process drone survey data for construction sites. Generate orthomosaics, DEMs, point clouds, calculate volumes, track progress, and integrate with BIM models for comparison.
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 and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
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。
Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
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。
Synchronize construction digital twins with real-time data. Connect BIM models with IoT sensors, progress updates, and field data for live project visualization and monitoring.
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。
Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.
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 querying or calling pay-per-token foundation models, or when creating, updating, listing, or querying Unity Gateway (also called Unity AI Gateway) services, including model provider services, MCP services, system.ai services, and three-part Unity Catalog service names (catalog.schema.service). Also use for managing spend budgets and usage alerts on Unity Gateway, and for migrating from legacy AI Gateway. Also use when a workload with Model Serving endpoint permissions (for example CAN_QUERY) gets PERMISSION_DENIED querying a three-part model name, since serving-endpoint access does not grant access to a Unity Gateway service. Not for configuring legacy AI Gateway on Model Serving endpoints; use databricks-model-serving for that.
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。
Quota-triggered failover from Claude Code, Codex, Cursor, Hermes or OpenClaw. When armed, it reads the sender quota and tries other-vendor models in place, then receiving agents in the configured order. Use when a long run must finish even if one vendor's quota runs out. Not a monitor for anything else.
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 estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.
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。
Account assignment by revenue potential, geography, relationship. Workload balancing, TAM/SAM calculation, coverage models.
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。
Designs pricing strategies for products and services. Takes product/service, costs, target market, competitors. Analyzes cost-plus, value-based, competitor-based, penetration, premium models. Researches competitor pricing. Generates pricing-strategy.md with recommended model, price points, tier structure, discount policies, annual vs monthly analysis, sensitivity to churn, expansion revenue modeling.
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。
Top-down vs bottom-up quota models. Historical attainment, market growth assumptions, ramp periods, territory complexity.
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。