'production data' 검색 결과

Generate Python code for Dataverse SDK workflows, including retries, bulk data changes, efficient queries, table metadata, and large file uploads. The skill also covers pandas integration and asks for type hints, docstrings, and official API references.

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• Implement retries for temporary Dataverse errors
• Create bulk data operations and efficient queries
• Manage custom tables and metadata caches

Generate Python code for Dataverse integrations with error handling, retries, logging, and shared connection management. Include type hints, usage examples, and queries that retrieve only the records and columns needed for the task.

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• Build Python integrations with Dataverse
• Add retries and logging to API operations
• Limit retrieved records and columns

Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.

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• Investigating errors in production
• Comparing error counts across time periods
• Following request traces across services

Build training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.

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Provision a PostgreSQL database with Neon Instagres and save connection settings for application use and migrations. Includes setup examples for Drizzle and Prisma, initial data loading, and claiming a temporary database to keep using it.

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• Provision a PostgreSQL database for development
• Configure database access with Drizzle or Prisma
• Load a SQL file with initial data

Convert laboratory instrument files into Allotrope Simple Model (ASM) JSON, a standardized data format, or tabular CSV. Detect instrument types, validate converted data, and export Python parsing code for analysis workflows or handoff to data engineers.

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• Standardizing instrument data for a laboratory management system
• Converting ELISA results into an importable CSV
• Exporting Python code to parse NanoDrop files

Use when reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

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Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

운영 데이터베이스의 열, 인덱스와 데이터 변경을 설계합니다. 대규모 테이블의 분할 갱신, 단계적 구조 전환과 복구 계획을 포함해 마이그레이션 절차를 정리합니다.

• 대규모 테이블에 열과 인덱스 추가
• 중단을 피하는 단계적 열 이름 변경
• 기존 데이터의 분할 보완

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

음악 제작 데이터의 ID로 메타데이터와 오디오 URL을 가져옵니다. 스템 분리, MIDI 추출, 마스터링 등 후속 처리의 최신 완료 상태를 확인합니다.

• 제작 ID로 곡 정보와 오디오 URL 조회
• 스템 분리 완료 여부 확인
• MIDI 추출 및 마스터링 상태 확인

Improve PostgreSQL performance through a staged workflow covering slow query analysis, indexes that speed up data access, query rewrites, configuration tuning, routine maintenance, and monitoring for production databases.

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• Investigating slow PostgreSQL queries
• Reviewing database indexes
• Tuning database configuration

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors

Debug "API returns data that doesn't exist in database" when multiple Kubernetes clusters exist (production, staging, POC). Use when: (1) REST API returns records that direct database queries can't find, (2) Data counts match approximately but specific records don't overlap, (3) kubectl context points to a different cluster than the one serving the public domain, (4) ClickHouse/Postgres queries return stale or different data than the API, (5) "completely different datasets" despite same table names and schemas. Root cause: querying the wrong cluster's database.

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You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data pipelines.

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Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, production database tables, or other data that PostHog does not collect natively. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, etc.), production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

공개 전이나 변경 병합 후에 근거를 바탕으로 앱의 인증, 데이터, 결제, 운영을 점검합니다. 출시를 막는 문제와 우선 수정 항목을 운영 준비 상태로 정리합니다.

• 공개 전 운영 준비 상태 확인하기
• 변경 병합 후 위험 확인하기
• CI 통과 후 운영 위험 점검하기

운영 환경의 동작과 장애 원인을 추적하도록 로그, 지표, 서비스 간 처리 경로와 알림을 설계합니다. 실제 출력과 알림까지 검증합니다.

• 운영 기능의 모니터링 정보 구성
• 서비스 간 로그 연결
• 알림과 대응 절차 검토