“databases”的搜索结果

搜索本地保存的Notion页面，查看数据库列表、报告和同步状态，用只读SQL统计页面数量，并在需要时更新数据或导出Markdown文件。

• 查找已保存的Notion页面
• 查看数据库列表和报告
• 用SQL统计已保存页面数量

Provision and manage Amazon RDS relational databases on AWS. Follow guidance for backups, recovery, security, monitoring, multi-zone redundancy, and read replicas—database copies that handle read traffic—with AWS CLI commands and a Terraform configuration example.

中文简介正在准备中。当前显示英语简介。

• Deploy a managed PostgreSQL or MySQL database
• Configure multi-zone redundancy and read replicas
• Set up backups and restore to a specific point in time

Plan and manage Cloud Spanner databases on Google Cloud, design tables and primary keys, write SQL and client code, and investigate slow queries using database statistics to identify resource-intensive operations.

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• Create Cloud Spanner instances and databases
• Design primary keys and related tables
• Write SQL queries and client library code

Create and administer AlloyDB for PostgreSQL databases, including clusters, instances, backups, secure connections, and access permissions. It also covers command-line operations, infrastructure configuration, and MCP tools that let agents automate database tasks.

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• Create an AlloyDB cluster and primary instance
• Manage backups and plan recovery
• Configure secure connections and access permissions

Search, create, and update records in Notion databases after checking the exact database and its fields. Preview proposed changes, preserve unrelated values, and verify the saved record with its URL or identifier.

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• Find records using the database’s actual fields
• Preview changes before creating or updating records
• Verify saved records and retrieve their links

Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

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ACSets (Attributed C-Sets): Algebraic databases with Specter-style bidirectional navigation. Category-theoretic formalism for relational databases.

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Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

中文简介正在准备中。当前显示原文说明。

Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.

中文简介正在准备中。当前显示原文说明。

搜索和更新Notion页面与数据库，以Markdown读写内容并上传文件。还可创建在Notion上运行的Workers，用于定期同步外部数据、提供智能体工具和接收外部事件。

• 将会议记录保存到Notion
• 按指定状态查找数据库条目
• 上传图片和资料

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

中文简介正在准备中。当前显示英语简介。

• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Identify and test critical workflows where APIs, services, databases, and interface components work together. Review normal behavior, important failures, and CI checks to find gaps that isolated unit tests can miss and keep those interactions continuously verified.

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• Testing interactions between APIs, services, and databases
• Checking components connected to state management
• Reviewing CI coverage for critical workflows

Plan and write literature reviews using multiple scholarly databases. Document searches, screen papers, synthesize findings, and check citations, then prepare Markdown or PDF reports with transparent selection counts, evidence limitations, and reproducible search records.

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• Prepare a literature review for a thesis or research paper
• Record reproducible searches across scholarly databases
• Track screening decisions and exclusion reasons

Analyze Android apps in a CTF sandbox using static inspection and Frida hooks, which observe or alter running code. Trace request signing, local storage, and Java-to-native boundaries to reproduce a request accepted by the server.

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• Tracing request-signing inputs and outputs
• Inspecting Java-to-native calls with runtime hooks
• Examining app settings, databases, and token storage

Build searches that find similar meanings using vector databases, which store numerical representations of data. Choose search indexes, combine semantic and keyword search, and tune retrieval quality and response time for search, recommendations, or AI answer systems.

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• Build search that finds documents with similar meanings
• Improve document retrieval for AI answers
• Design a recommendation engine

Build AI applications that answer questions using retrieved documents. Covers vector databases, text embeddings that represent meaning as numbers, search strategies, and result reranking to connect language models with external knowledge.

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• Building Q&A systems for internal documents
• Adding domain knowledge to chatbots
• Implementing natural language document search

Build a Java MCP server, a service that lets AI agents call Java code, APIs, or databases. The skill prepares a Maven or Gradle project, implements tools, adds tests, and configures client connections.

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• Expose existing Java code to AI agents
• Connect agents to APIs or databases
• Build an MCP server in Spring Boot

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

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• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Analyze Windows Amcache records to investigate applications, deleted executable traces, and drivers. Extract file details, compare SHA-1 hashes with malware databases, and combine records into a timeline to support incident investigations.

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• Investigating traces of deleted executables
• Checking software installed during an incident
• Comparing file hashes with malware databases

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

Build LlamaIndex applications that retrieve relevant material before generating answers, a method called RAG. Connect documents, websites, and databases to support document questions, conversational search, structured summaries, and checks of answer relevance and source consistency.

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• Building question answering over internal documents
• Creating a chatbot that remembers the conversation
• Searching information across files, websites, and databases

Ask questions about your journal, Obsidian vault, or other text notes, and continue conversations that remember earlier chats. The companion uses cognee memory with local databases, reads your notes without changing them, and saves conversations for later reference.

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• Asking questions about journal entries
• Finding information in an Obsidian vault
• Continuing a conversation with earlier chats in memory

Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph