'apa' 검색 결과

Review Apache Pinot changes affecting frequently executed code or making performance claims in a pull request. Relevant areas include operators, segment readers, aggregation functions, and locking inside loops that process individual rows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review changes to frequently executed processing code
• Review performance improvement claims in a pull request
• Review locking inside per-row loops

Review Apache Pinot changes against naming and API conventions, including public names, method signatures, REST endpoints, JSON fields, and documentation. Use it when adding or renaming elements exposed to other code or users.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing public class, method, or field names
• Checking new enum constants or DataType values
• Reviewing REST endpoints and JSON fields

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Draft and check a systematic literature review using PRISMA 2020, a reporting guideline for transparent reviews. Organize methods and findings into a Word manuscript, create a study selection flow diagram, and format references in APA 7th Edition style.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a first systematic literature review
• Turning a protocol or extraction sheet into a manuscript
• Creating a PRISMA study selection flow diagram

Analyze current trends and challenges in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a combined trend + issue report. Use only when the user explicitly wants a trend/landscape report, a challenges/limitations report, or a general research overview of a Japanese NLP topic (this combines the bundled dataset with live web research). Trigger phrases include '日本語LLMの最新トレンド', '〜の動向をまとめて', '最近の日本語NLPの流れ', '日本語LLMの課題', '〜の問題点・限界', '未解決の論点', 'trend report on Japanese embeddings', 'latest Japanese speech models', 'challenges in Japanese NER', 'limitations of Japanese embeddings'. For a simple lookup use the search skill; this one runs web research.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quarkus 3.x LTS의 기능 추가와 오류 수정을 테스트부터 진행합니다. API, 이벤트, 비동기 처리를 검증하고 테스트를 통과하는 구현과 커버리지 확인을 지원합니다.

• 테스트부터 작성해 REST API 추가
• 오류 재현 및 수정
• Apache Camel 경로와 이벤트 처리 검증

Analyze Apache and Nginx access logs for attack attempts, scanner activity, and repeated login requests. Add geographic context to source IP addresses and produce a prioritized findings report to support intrusion investigations and monitoring reviews.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating suspicious requests in web access logs
• Finding injection attempts and unauthorized file access attempts
• Identifying scanners and repeated login attempts

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Write and review Apache SeaTunnel unit tests and E2E tests that check complete jobs using Testcontainers. It provides concrete rules for container setup, waiting, resource cleanup, and assertions to address intermittent failures and resource leaks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing a new connector E2E test
• Reviewing a test diff before a pull request
• Fixing intermittent failures or resource leaks in tests

Audit one datafusion-spark function against Apache Spark 4.2.0. Compare source code, accepted types, and edge cases, verify expected results with PySpark, then fix differences and record remaining gaps as issues and disabled tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing a function for Spark compatibility
• Checking NULL, overflow, and ANSI mode behavior
• Finding missing SQL test coverage

Work with Apache Beam I/O connectors, components that read and write external data. Get guidance on connector usage, unit and integration tests, troubleshooting data connections, and the basic structure of new connectors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add BigQuery or Kafka reads and writes to a pipeline
• Run unit and integration tests for connectors
• Investigate data source or destination issues

Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark execution settings

Guides Java SDK development in Apache Beam, from finding relevant modules to building code, running focused tests, and trying examples. Also explains how to publish modified modules locally and build SDK containers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding the Java SDK module to modify
• Running a specific test class or method
• Checking setup with the WordCount example

Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding trace context or drain flags to WindowedValue
• Checking metadata preservation through Windmill storage
• Reviewing propagation through grouping and timers

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Helps investigate and modify Apache Beam's automated build and test workflows in GitHub Actions. Explains workflow types, failure logs, targeted reruns, local reproduction, and the cloud credentials needed for workflows that access GCP.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failed checks on a pull request
• Rerunning a specific test workflow
• Reproducing CI test commands locally

Review the scope and process of Apache Pinot changes, including pull request descriptions, reverts, retries, and compatibility notes. Use it to examine large changes, test workarounds, and documentation needed for changes that break backward compatibility.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing large changes across multiple modules
• Reviewing Revert or Hotfix pull requests
• Checking retries and sleeps added to tests

Review Apache Pinot changes for concurrency and state safety, including locks, shared callbacks, lifecycle changes, and distributed metadata. Use it to examine how changes affect shared state when multiple operations run at the same time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes to locks and atomic operations
• Checking shared callback registration changes
• Reviewing Helix state transitions and ZK node changes

Review regression coverage and test quality in Apache Pinot when behavior or tests change. Use it to assess whether code changes and bug fixes have relevant tests, including paths that handle different types or null values.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing added or modified test files
• Checking production changes without test updates
• Checking regression tests for bug fixes

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.

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