「databases」の検索結果

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。

Search, read, create, and update Notion pages and databases. Supports knowledge capture, meeting prep, research documentation, and spec-to-task workflows.

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.

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。

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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。

SQL query optimization, schema design, indexing strategies, and relational database mastery for production data systems

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。

MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications

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。

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

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。

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

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。

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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。

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

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。

Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.

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。

Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.

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。

Implementing Microsoft Defender for Cloud to enable cloud security posture management, workload protection across VMs, containers, databases, and storage, configure security recommendations, and Kur: adaptive security controls with automated remediation.

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。

Implementing Cloud Data Loss Prevention (DLP) using Amazon Macie, Azure Information Protection, and Google Cloud DLP API to discover, classify, and protect sensitive data across cloud storage, databases, and data pipelines.

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。

bu skill covers Dağıt:ing HashiCorp Vault for centralized secrets management across cloud environments, including dynamic secret generation for databases and cloud providers, transit encryption, PKI certificate management, and Kubernetes integration. It addresses eliminating hardcoded credentials from application code and CI/CD pipelines by implementing short-lived, automatically rotated secrets.

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。

bu skill instructs security practitioners on Dağıt:ing Microsoft Defender for Cloud as a cloud-native application protection platform for Azure, multi-cloud, and hybrid environments. It covers enabling Defender plans for servers, containers, storage, and databases, configuring security recommendations, managing Secure Score, and integrating with the unified Defender portal for centralized threat management.

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。

Notion API + ntn CLI: pages, databases, markdown, Workers.

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。

Conducts external reconnaissance using Open Source Intelligence (OSINT) techniques to map an organization's external attack surface without directly interacting with target systems. The tester gathers information from public sources including DNS records, certificate transparency logs, search engines, social media, code repositories, and data breach databases to build a comprehensive target profile. Activates for requests involving OSINT reconnaissance, external footprinting, attack surface mapping, or passive information gathering.

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。

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.

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。

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

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。

Create new scientific tools for ToolUniverse framework with proper structure, validation, and testing. Use when users need to add tools to ToolUniverse, implement new API integrations, create tool wrappers for scientific databases/services, expand ToolUniverse capabilities, or follow ToolUniverse contribution guidelines. Supports creating tool classes, JSON configurations, validation, error handling, and test examples.

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。

KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.

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。

Biological knowledge: query curated assertions and reference databases for genes, variants, pathways, regulation, cell types, reference atlases, perturbation-derived relationships, phenotypes, therapeutic targets, drugs, interactions, annotations, signatures, compounds, and predicted structures. Use for identifiers, marker sets, gene sets, associations, ontologies, database facts, and cross-database coverage.

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。