'production data' 검색 결과

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

Generate Drizzle database migrations on a fresh Neon branch and review their production safety. Check table sizes, locking order, execution time, and data transfer needs, with load testing for changes affecting busy or large tables.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate migrations after changing a database schema
• Review locking risks before production deployment
• Rehearse large table changes under load

Plan AI-assisted SEO, from keyword research and competitor comparison pages to content production using data and templates. It also guides content changes for AI search citations, tool selection, quality checks, and ongoing performance tracking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning competitor alternative and comparison pages
• Designing page production using data and templates
• Reviewing content for citations in AI Overviews

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

운영 데이터베이스의 구조 변경과 기존 데이터 이전을 계획합니다. 열과 인덱스 추가, 단계적 전환, 복구 절차를 정리하고 중단 시간과 잠금을 고려한 변경을 돕습니다.

• 운영 테이블에 열이나 인덱스 추가
• 대량 데이터의 분할 갱신
• 앱을 중단하지 않고 열 교체

Review Entity Framework Core code and get improvements for database models, queries, saving changes, and migrations, which track database schema changes. Covers performance, security, testing, and how database access code is organized.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review database models and relationships
• Improve slow queries and avoid repeated database calls
• Check migrations before production deployment

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Audit a codebase before production deployment across eight areas, including security, databases, dependencies, and AI usage. Receive findings with file locations, manual checklists, and a release verdict that blocks shipping when critical issues remain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check an application before its first production release
• Find security and database issues before deployment
• Review backup restoration and rollback readiness

Review plans involving customer data, compliance, or production access. Examine threats, potential damage, detection, incident response, notification requirements, and vendors, then summarize whether the plan can proceed, needs mitigation, or should be blocked.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a feature that handles customer data
• Preparing for a SOC 2 or ISO 27001 audit
• Assessing a vendor that will access data

Manage which n8n instance an MCP session targets when working across production, staging, teams, or clients. Guides instance switching, checks before credential changes, and recovery from unexpected missing data or ambiguous credential writes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switching between production and staging
• Checking the target before credential changes
• Investigating unexpected missing data

Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating failures in LLM applications
• Evaluating answers against test datasets
• Monitoring production latency and costs

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Rehearse Convex schema changes on a deployment seeded with a production snapshot. Populate existing records, verify data and behavior, then repeat the proven sequence in production with explicit approval, keeping the snapshot available for rollback.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rehearse schema changes using a copy of production data
• Populate existing records before requiring a new field
• Verify migrated data and app behavior before production

Build Next.js 14+ applications with App Router, covering pages, server-side processing, data fetching, and caching. The skill also guides performance improvements, search engine metadata, and production deployment to Vercel or a self-hosted environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building pages and layouts with App Router
• Implementing form updates with Server Actions
• Configuring data fetching and cache revalidation

Fine-tune NV-Generate-CTMR MR-Brain v1 using supplied brain MRI volumes. Check the input file list, prepare training configurations, run training, and optionally generate sample volumes, producing a saved model and a structured run summary.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check a brain MRI dataset before GPU training
• Fine-tune a model with T1, T2, FLAIR, SWI, or MRA data
• Prepare a training command with explicit data and output paths

Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases. **Trigger when user asks to:** - Test a schema migration before applying it to production - Add, remove, or rename columns safely on a live table - Change a column's data type without downtime - Add or drop indexes, constraints, or foreign keys on large tables - Understand which ALTER TABLE operations lock the table - Roll back a failed migration - Plan a zero-downtime migration strategy - Fork a database to test a migration safely **Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy Covers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

앱의 미완성 코드와 임시 구현을 찾고 실제 데이터베이스 및 외부 API 연동을 검증합니다. 동시 접속 성능, 인증과 배포 설정도 점검해 출시 준비 상태를 확인합니다.

• 출시 전 미완성 코드 찾기
• 실제 데이터베이스와 API 연동 검증
• 동시 접속 시 성능 확인

Set up or review production error monitoring to capture unhandled errors, failed requests, and performance signals. Check event context, release labels, and alert routing so teams receive useful information when problems occur.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up error monitoring for a new production app
• Investigating errors that monitoring fails to capture
• Reviewing critical alerts and on-call routing

Implement Python services for Azure Cosmos DB NoSQL with guidance for authentication, data models, database operations, and tests. Separate business logic from storage access, validate data access permissions, and use reusable templates for consistent implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding Cosmos DB storage to a FastAPI application
• Separating database access from business logic
• Configuring Azure and local emulator authentication