362 件 ・ 関連度順
概要と使いどころ
Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes. Triggers: "BatchClient java", "azure batch java", "batch pool java", "batch job java", "HPC java", "parallel computing java".
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。
Azure Maps SDK for .NET. Location-based services including geocoding, routing, rendering, geolocation, and weather. Use for address search, directions, map tiles, IP geolocation, and weather data. Triggers: "Azure Maps", "MapsSearchClient", "MapsRoutingClient", "MapsRenderingClient", "geocoding .NET", "route directions", "map tiles", "geolocation".
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。
Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall parameters.
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 a user asks an agent to evaluate or tune text search similarity for an existing Viking AI Search application and dataset.
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。
Answer Viking AI Search product questions, CLI usage questions, API/auth questions, configuration questions, and troubleshooting questions by grounding every claim in either the installed `vs` CLI's own output or official Volcengine documentation. Never fabricate.
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。
Crawl websites (news, blogs, papers, GitHub, product docs, RSS feeds) into a fixed-schema JSONL file, then create a dataset and a searchable application in Viking AI Search. Supports one-time crawl and scheduled recurring crawl with automatic incremental sync.
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。
Conversational search runtime: send messages, keep sessions consistent, and verify retrieval behavior and responses.
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。
Provide system alias mapping for Search CLI. Invoke this skill when user mentions "Search CLI", "search_cli", or tries to execute search_cli commands.
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。
Volcengine official documentation lookup helper. Supports both document search and full-content fetch across Volcengine products, developer tools, support content, best practices, pricing, deployment, troubleshooting, API, SDK, and policy pages.
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 Viking web projects, start and verify a local preview, or deploy a generated project to Volcengine IGA Pages when explicitly requested. Includes agent-guided feature, eligible application, dataset, scene, and authentication choices. Use only after confirming the installed CLI exposes `vs project`; otherwise stop without taking action.
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。
onboarding workflow for creating datasets and applications in Viking AI Search. Supports one-time import from local files (JSON, JSONL, CSV) and MySQL databases, plus scheduled incremental sync for append-only JSONL files and MySQL. All sources are first exported to a bootstrap JSONL file; backend-driven schema inference handles detection, and optional background sync keeps the dataset up to date as new data arrives.
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 the user provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay script. It must not mutate scenes, apps, dictionaries, datasets, recall core parameters, or online defaults.
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。
Recommend runtime and V2 scene management: run recommendation requests, manage recommend scenes and rules, and verify the deployed recommendation path.
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。
Search runtime and scene management: verify queries, inspect scenes, debug app readiness, and diagnose recall or scene-config issues.
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。
This skill should be used when user asks to "deploy to Hetzner", "create Hetzner server", "manage Hetzner Cloud", "hcloud CLI", or works with Hetzner Cloud infrastructure including servers, networks, firewalls, load balancers, DNS zones, and volumes.
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。
Comprehensive guide for writing and running Terraform tests. Use when creating test files (.tftest.hcl), writing test scenarios with run blocks, validating infrastructure behavior with assertions, mocking providers and data sources, testing module outputs and resource configurations, or troubleshooting Terraform test syntax and execution.
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 Terraform HCL code following HashiCorp's official style conventions and best practices. Use when writing, reviewing, or generating Terraform configurations.
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。
Review a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.
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。
Translate a complete mathematical modeling contest problem sentence by sentence into precise modeling language, preserve every substantive condition and definition, expose hidden constraints, draw a mandatory Mermaid cross-question flowchart, audit omissions, and deliver one easy-to-open Markdown report. For Huawei Cup (CPGMCM) graduate-contest problems, additionally prepend a plain-language layer that rewrites the background and questions as a primary-school word problem with a mandatory term-mapping table. Optionally render the finished report as one standalone HTML page on request. Use whenever a user supplies a CUMCM, CPGMCM, or other modeling problem and asks to interpret, translate, unpack, read closely, identify requirements, or avoid missing details before 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。
Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.
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 a compliant Chinese mathematical-modeling problem-restatement chapter from a complete problem statement, or review a user's restatement against the original problem. Covers research background, problem review, and research overview; does not perform the separate problem-analysis chapter.
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。
Review a user-provided Chinese mathematical-modeling abstract without requiring the problem statement or full paper, identify concrete writing defects, and prioritize revisions. Use when the user asks to check, diagnose, or improve a modeling-paper abstract; do not claim coverage or factual consistency that cannot be verified without the problem or paper.
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。
Review mathematical modeling competition papers from a complete problem and paper, with an optional strict bzd-paper-format-checker audit, itemized problem-specific scoring, a bottom-up low-score safeguard, and CUMCM award calibration by problem type, division, region, school and advisor. Generate a Chinese HTML judge report.
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 or review mathematical-modeling competition AI tool usage statements and the anonymous AI工具使用详情.pdf. Use when users provide real AI usage records, or when checking a paper, statement, details PDF, code, and AI records for completeness, anonymity, and consistency.
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。