'apa' 검색 결과

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Given a Japanese NLP GitHub repo/model/dataset (URL / owner/repo / tool name) OR a topic, find what's already in awesome-japanese-nlp-resources and discover related resources NOT yet listed (contribution candidates). Mines the bundled dataset, then expands via web research across GitHub and Hugging Face. Use when the user names a SPECIFIC repository, model, or tool and wants alternatives/equivalents, OR wants to discover Japanese NLP resources for a topic that are NOT yet in the list, OR wants to prepare a contribution. Trigger phrases include 'mecabに似たツール', 'fugashiの代替', 'alternatives to fugashi', 'repos like manga-ocr', 'what else is like sudachi', 'リストに無い新しい日本語NLP', 'awesome-japanese-nlpに追加できそうな', '最近公開された日本語NLPツール', 'find unlisted Japanese NLP repos', 'new Japanese models on Hugging Face', 'contribute a new resource'. For a simple lookup of what already exists, use the search skill instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Format a Quarto or R Markdown report following APA 7th edition style. Covers apaquarto/papaja packages, title page, abstracts, citations, tables, figures, and reference formatting. Use when writing an academic paper in APA format, creating a psychology or social science research report, generating reproducible manuscripts with embedded analysis, or preparing a thesis or dissertation chapter.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

제조 현장의 규격 부적합품에 대해 격리, 원인 분석, 처분 검토, 시정·예방 조치의 효과 확인을 정리하고 관리도 해석과 공급업체 품질 개선을 지원합니다.

• 검사에서 발견한 부적합 문제 조사
• 근본 원인 분석 방법 선택
• 시정 조치 계획과 효과 확인

Helps add or update translations for the Apache Airflow interface. Covers language setup, translation file generation, consistent terminology, preserved variables and plural forms, and checks for missing, unfinished, or unused entries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new interface language to Airflow
• Filling gaps in an existing translation
• Aligning terminology and preserving translation variables

Draft commit messages, pull request titles and descriptions, and GitHub comments or reviews for Apache Airflow contributions. Follows project wording and AI attribution rules, and helps decide whether a change needs a release note.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft commit messages and pull request titles
• Prepare pull request descriptions using the project template
• Draft GitHub comments and review text

Update Flask-AppBuilder in Apache Airflow’s FAB provider, including pinned versions, dependency locks, and documentation. Check compatibility tests and review copied security code, with asset and database migration updates when the upstream release requires them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Upgrade the FAB provider to a newer Flask-AppBuilder release
• Review upstream authentication fixes against Airflow's copied code
• Resolve compatibility failures after a dependency update

Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow Spark jobs
• Tuning memory and executor settings
• Reducing data skew and shuffle traffic

Build Apache Airflow workflows for data pipelines and scheduled jobs. The skill guides task dependencies, retries, waiting for conditions, testing, and deployment, with patterns for tasks that can be safely rerun.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building scheduled data pipelines
• Designing task order and dependencies
• Implementing custom tasks and condition checks

Build intermediary servers for authorized security exercises, separating public traffic from the command server used by the test team. It covers simple forwarding, request filtering with nginx or Apache, TLS certificates, and validation against the exercise’s traffic profile.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Separating public infrastructure from an exercise command server
• Filtering exercise traffic against a defined profile
• Adding TLS certificates to a redirector

Size support and operations teams using demand ranges and queueing calculations, which estimate how work waits for service. Assess workload imbalance and service target risks, then build a quarterly hiring plan that accounts for training time and attrition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate staffing for support and operations queues
• Check workload imbalance and service target risks
• Build quarterly hiring plans with ramp time and attrition

Route internal operations questions to six specialist skills covering workflows, vendors, staffing capacity, communications, procedures, and procurement. Receive a brief summary of findings, recommended actions, and links to the documents or data files produced.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in internal workflows
• Reviewing vendor performance and purchasing costs
• Planning staffing capacity for the next quarter

Helps create and extend Apache Airflow DAGs, workflows that define task order and dependencies, for Managed Service for Apache Airflow. Covers environment checks, version compatibility, coding practices, and local or deployed validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a DAG for a managed Airflow environment
• Adding tasks to an existing workflow
• Checking DAG import errors before deployment

Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enable automatic scaling for GKE node pools
• Find why nodes do not scale up
• Identify constraints that prevent nodes from scaling down

Investigate Apache ShardingSphere issues using official documentation, code, tests, and issue discussions. Distinguish bugs, unsupported usage, duplicates, and enhancement requests, then draft a concise maintainer-style reply explaining the evidence and next action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Distinguish bugs from unsupported configurations
• Check whether an issue duplicates an earlier report or fix
• Draft a GitHub issue reply with clear next steps

Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing data ingestion and transformation workflows
• Processing only new or changed records
• Adding data quality checks between processing stages

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Write Apache Cassandra distributed tests that simulate multiple nodes within one JVM, the Java runtime. The guide covers cluster setup, queries, node restarts, message dropping for failure scenarios, and troubleshooting problems caused by isolated class loading.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing read and write tests across multiple nodes
• Testing timeouts by dropping messages between nodes
• Verifying node replacement and restarts

Prepare English pull requests for Apache SeaTunnel from a change summary, branch history, and file differences. It builds titles and bodies that follow project conventions, records actual testing, and can submit the pull request when requested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting an English PR from current branch changes
• Choosing a PR title that follows SeaTunnel conventions
• Completing the PR template with changes and actual test results

Review Apache DataFusion pull requests using the project's review guide. Gather the proposed changes, discussion, and automated check results, or apply the same review criteria to your own changes before submitting a pull request.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an Apache DataFusion pull request
• Checking your changes before submitting a pull request
• Reviewing changes alongside discussion and automated check results

Inspect an Apache Hudi table’s storage layout using a read-only analysis tool. Get report-based findings on table size, uneven data distribution, excessive partitions, small files, and concentrated writes, with suggested next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check table size and the largest partitions
• Identify accumulated small files
• Assess excessive partitioning and uneven data distribution

Check whether Apache Hudi table maintenance is keeping pace with incoming data. Runs a read-only utility, interprets its JSON report, and explains unhealthy or skipped checks with concrete next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking Hudi table maintenance health
• Investigating delayed compaction or cleaning
• Finding why the active timeline keeps growing

Design or review Apache Hudi tables through questions about data volume, updates, and usage. Get architecture recommendations, a decision record explaining tradeoffs, and configuration artifacts for supported processing paths.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new Hudi table
• Reviewing an existing table design
• Planning a small prototype

Guides Apache OpenDAL release managers through preparation, release candidates, verification, voting, and publication. It tracks the current stage, checks external systems, and distinguishes core, candidate, and package versions when deciding the next release action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a weekly candidate or manual release candidate
• Checking release CI and staged source artifacts
• Preparing a vote and verifying its outcome