「resource」の検索結果

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

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 Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.

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 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".

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。

What-if analysis for construction projects: model different scenarios and their cost/schedule/resource impacts. Compare alternatives and optimize decisions.

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。

Semantic search in the DDC CWICR construction cost database using vector embeddings (BGE-M3, 1024-dim, per-language Qdrant collections). Find similar work items and resources for cost estimation across 8 national bases and 30 markets in 26 languages.

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。

Allocate and track labor resources across project activities. Balance workload, track attendance, and optimize crew assignments.

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。

Update CWICR resource rates with current market prices. Integrate external price data, apply inflation adjustments, and maintain rate history.

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。

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.

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。

Plan organizational capacity for construction projects. Forecast resource needs, identify capacity gaps, and support strategic planning for project pursuit and staffing.

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 labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum efficiency.

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。

Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.

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。

Drive the agentkit CLI (aliases agentkit / ak) to scaffold, build, deploy, and manage agents on Volcengine AgentKit from the terminal — runtimes, knowledge bases, memory collections, tools, harnesses, invocation, SSO auth, sandboxes, and evaluation datasets. Use when a user wants to create/deploy an agent project, call or inspect a deployed runtime, manage AgentKit resources, or resolve a CLI/auth/deploy error. Does NOT handle non-AgentKit deployment, general Docker/k8s operations, Volcengine IAM management, or writing agent application code (use veadk-agent-development 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。

Maintain project state across MULTIPLE conversations over days/weeks. Track tasks, decisions, blockers, resources. Auto-update when project mentioned. Generate status reports and proactive reminders.

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。

Scaffold, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.

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。

Tracks quotas, monitors thresholds, and degrades gracefully for rate-limited APIs. Use when integrating external services that impose rate or cost limits.

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。

Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

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 or update a Codex skill with appropriately scoped instructions and any needed supporting resources.

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。

Operate and extend GeoNode geospatial content-management deployments, resource APIs, uploads, metadata/catalogues, GeoServer security, harvesting, and administration with explicit service and safety gates.

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 the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction.

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 working with Dagster OSS: assets, jobs, Definitions, config/resources, schedules/sensors, automation, CLI/local development, deployment operations, GraphQL/webserver, Pipes external processes, components/projects, or editing the Dagster repository itself.

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。

Routes tasks for discovering NLP datasets in the CLUEDatasetSearch catalogue, including Chinese NLP classification, QA, matching, NER, generation, translation, corpus, and reading-comprehension resources.

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。

Route Chinese-BERT-wwm model loading, model selection, benchmark data, and troubleshooting workflows for HFL Chinese BERT-family resources.

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。