「knowledge base」の検索結果

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

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Expert knowledge for Azure SQL Managed Instance development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring MI networking, backups/restore, geo-replication/HA, Entra/Kerberos auth, or MI Link workloads, and other Azure SQL Managed Instance related development tasks. Not for Azure SQL Database (use azure-sql-database), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db).

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Expert knowledge for SQL Server on Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning SQL VM vs Managed Instance, Always On/FCI HADR, DTU/capacity limits, Key Vault/MI security, or Blob backups, and other SQL Server on Azure Virtual Machines related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines), Azure Data Science Virtual Machines (use azure-data-science-vm).

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Expert knowledge for Azure Oracle development including troubleshooting, security, and integrations & coding patterns. Use when configuring Oracle TDE with Azure Key Vault, fixing Oracle@Azure issues, or exporting Exadata logs to Azure Monitor/Sentinel, and other Azure Oracle related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), SAP HANA on Azure Large Instances (use azure-sap).

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 knowledge for Azure Migrate development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when sizing and migrating VMs/SQL/PostgreSQL, configuring appliances/Arc, using AppCAT/CAST, or Private Link, and other Azure Migrate related development tasks. Not for Azure Database Migration Service (use azure-database-migration), Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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 knowledge for Azure Policy development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when authoring JSON policies, deploying via ARM/Bicep/Terraform, enforcing security baselines, automating with Gatekeeper, or running policy-as-code in CI/CD, and other Azure Policy related development tasks. Not for Azure Blueprints (use azure-blueprints), Azure Role-based access control (use azure-rbac), Azure Resource Manager (use azure-resource-manager), Azure Security (use azure-security).

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 knowledge for Azure DocumentDB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Mongo-compatible APIs, vector search, cross-region replication, change streams, or the HTTPS Data API, and other Azure DocumentDB related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).

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 knowledge for Azure Horizondb development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when tuning pgvector, azure_ai SQL functions, LangChain vector stores, Apache AGE graphs, or hybrid search, and other Azure Horizondb related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure SQL Database (use azure-sql-database), Azure Table Storage (use azure-table-storage).

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 knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building indexes, skillsets, indexers, vector/semantic search, or RAG knowledge bases with Azure AI Search, and other Azure AI Search related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Cosmos DB (use azure-cosmos-db).

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 knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).

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 knowledge for Azure Analysis Services development including troubleshooting. Use when resolving server connectivity, firewall/VNet, DNS, client connection, or network error issues, and other Azure Analysis Services related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

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。

Design or audit a repo-local markdown knowledge graph with wiki links, source-code backlinks, drift checks, and searchable sections. Use when AGENTS.md/CLAUDE.md is too flat for a large codebase or when a custom harness needs durable structured project memory.

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Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph 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。

Display knowledge base status and recent learnings

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Builds an Open Knowledge Format (OKF) knowledge base from existing docs, notes, or a repo. Use to scaffold an OKF wiki.

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。

Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

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。

Presents adaptive codebase challenge questions with multiple-choice and trace exercises. Use when testing contributor knowledge of the codebase.

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。

Adds developer-authored annotations to the gauntlet knowledge base. Use when capturing tribal knowledge or rationale not visible in code.

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。

Writes and restructures technical documentation - READMEs, tutorials, how-to guides, user guides, architecture docs, onboarding guides, runbooks and SOPs, troubleshooting guides, release notes, and knowledge base articles - grounded in the actual code or product, with commands and examples verified where possible. Use when the user asks to document a project, feature, system, or process, write or fix a README, explain a codebase to new developers, turn notes into a guide, or make existing docs clearer. For endpoint-by-endpoint API reference or OpenAPI specs, use api-documentation-writer; for CLAUDE.md or AGENTS.md files, use claude-md-writer.

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n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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Export, write, and manage Feishu/Lark cloud documents. Supports docx, sheets, bitable, wiki, WeChat article import/export, drive management, and browser-based export for public or browser-readable docs. Use this skill when you need to read, analyze, write, or manage content in a Feishu knowledge base.

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Write and maintain knowledge base articles from resolved support issues. Use when a ticket has been resolved and the solution should be documented, when updating existing KB articles, or when creating how-to guides, troubleshooting docs, or FAQ entries.

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Research any topic in exhaustive depth by combining web search, academic papers, social discussions, and community conversations. Produces comprehensive briefings with evidence from every available source type, confidence scoring, and identified knowledge gaps.

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。