「databases」の検索結果

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.

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。

Parse SQLite databases into structured JSON data. Use when exploring unknown database schemas, understanding table relationships, and extracting map data as JSON.

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 for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

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。

Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.

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。

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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。

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.

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。

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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。

Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.

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。

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.

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。

Synthesize durable ktx wiki pages and semantic-layer sources from staged Notion pages, databases, data-source rows, and clustered Notion evidence. Load when a WorkUnit contains Notion raw files or Notion evidence chunks.

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 Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake. Answers questions about storage access tiers (hot, cool, cold, archive), when to use each tier, and tier comparison. Provides object storage, SMB file shares, async messaging, NoSQL key-value, and big data analytics. Includes lifecycle management. USE FOR: blob storage, file shares, queue storage, table storage, data lake, upload files, download blobs, storage accounts, access tiers, storage tiers, hot cool cold archive, storage tier comparison, when to use storage tiers, lifecycle management, Azure Storage concepts. DO NOT USE FOR: SQL databases, Cosmos DB (use azure-prepare), messaging with Event Hubs or Service Bus (use azure-messaging).

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。

Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data 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。

Work with Notion: search pages, read page properties, query databases, create pages, append blocks, update page properties. Trigger phrases: notion, notion page, notion database.

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。

Inspect Neon serverless Postgres: projects, branches, databases. Branch create and delete need exact-match confirmation; connection passwords are masked. Trigger phrases: neon, serverless postgres, database branch, postgres project.

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。

Infrastructure-as-code specialist for multi-cloud provisioning using Terraform across any provider (AWS, GCP, Azure, Oracle Cloud). Use for terraform plan/apply, state management, compute, databases, storage, networking, IAM, OIDC, cost optimization, policy-as-code, ISO/IEC 42001 AI controls, ISO 22301 continuity, and ISO/IEC/IEEE 42010 architecture documentation.

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。

Backend specialist for APIs, databases, authentication with clean architecture (Repository/Service/Router pattern). Use for API, endpoint, REST, database, server, migration, and auth work.

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。

Authors and queries Fabric Eventhouse and KQL databases: tables, functions, policies, materialized views, ingestion, and read-only KQL for real-time and time-series analytics. All KQL database work belongs here, table creation and simple queries included. An Eventstream item is eventstream-cli; an Event Schema Set is eventschemaset-cli.

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。

Manage Fabric Warehouse, Lakehouse SQL endpoints, and Mirrored Databases: DDL/DML, COPY INTO, read-only T-SQL, Query Insights diagnostics, and Capacity Metrics CU-spike correlation. Synapse migration target SQL belongs to synapse-migration; Fabric SQL database belongs to sqldb-cli. Triggers: query warehouse, create warehouse table, failed or canceled query, CU spike, Capacity Metrics app, custom SQL pool, Lakehouse table health.

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。

Converts Synapse workspaces to Fabric: Spark items, Lake Databases, Linked Services, and Dedicated SQL Pool schema/procedures for Lakehouse or Warehouse. During Pool-to-Lakehouse migration only, can migrate approved dependent procedure callers after notebook readiness. Excludes standalone pipelines and Warehouse administration. Triggers: Synapse workspace to Fabric, Dedicated SQL Pool to Lakehouse, Dedicated SQL Pool to Fabric Warehouse.

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。

Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.

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。

Write integration tests using TestContainers for .NET with xUnit. Covers infrastructure testing with real databases, message queues, and caches in Docker containers instead of mocks.

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。

Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.

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。

Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.

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。

Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.

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。