'knowledge base' 검색 결과

Organize papers from Zotero into an Obsidian project knowledge base. The skill separates source notes, research synthesis, and writing outputs, requiring evidence records and source links before findings become claims used in later work.

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• Organizing Zotero papers into project source notes
• Connecting literature findings to their supporting evidence
• Preparing evidence-linked research writing

Turn validated experiment analysis into an internal research report that supports decisions. Organize findings, statistical evidence, figure interpretations, limitations, and next actions, then save the report as Markdown or write it into a connected Obsidian knowledge base.

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• Write a wrap-up report from validated experiment analysis
• Explain figures and their implications for decisions
• Document negative results, limitations, and next actions

Format Obsidian notes, repair internal links, and create registry tables and canvas diagrams. The skill handles formatting and derived files while leaving decisions about where knowledge belongs outside its scope.

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• Standardizing Markdown formatting in Obsidian notes
• Repairing links in existing canonical notes
• Creating and validating canvas diagrams

Turn an AI coding session into a concise team learning document. Summarize the problem, decisions, solution, useful tools, and pitfalls, then publish the Markdown document to the team knowledge base through TeamAI.

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• Document the solution to a difficult problem
• Share a useful workflow or pattern
• Capture decisions and pitfalls from a long session

Combine Obsidian notes into literature reviews, comparisons, and project summaries. Keep each synthesis linked to its supporting papers, experiments, results, and research questions, with one lasting note for each purpose.

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• Combine paper notes into a literature review
• Compare findings across experiments
• Summarize a project's progress and results

Coordinate a seven-stage literature review, from defining a research question to collecting and screening papers, building a searchable knowledge database, and producing PRISMA documentation. Specialized agents handle execution, with human approval at key decisions.

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• Managing a systematic literature review
• Building a research knowledge repository
• Tracking approval of paper selection criteria

Document an incident with a sourced timeline, clear user impact, and analysis that separates symptoms, triggers, and underlying causes. Produce a blameless review with action items that specify responsible teams, deadlines, and checkable completion criteria.

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• Write an outage report grounded in logs and alerts
• Separate incident triggers from underlying causes
• Compare recurring incidents and previous corrective actions

Learn programming through tutorials tailored to your experience, goals, and actual project code. Keep a shared learning history across projects, record questions, and use quizzes with spaced review to revisit concepts based on demonstrated understanding.

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• Learning concepts through code from an existing project
• Choosing what to study next based on previous learning
• Reviewing weak concepts through quizzes

Document a project's purpose, technical choices, and organization patterns as persistent reference files for agents. Compare existing guidance with the codebase, identify mismatches, and propose additions while preserving user-written content.

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• Create project reference documents from an existing codebase
• Check whether project guidance matches current code
• Document technical and organizational conventions

Create a dated Markdown reference for an active Jetson target profile. It summarizes board support package and source directories, relevant configuration files, and document references, checking local paths so later sessions can consult a saved snapshot.

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• Documenting the layout after BSP extraction
• Refreshing the reference after source updates
• Checking configuration files for a target module

Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways), diagram export, cross-reference mapping, or searching the knowledgebase.

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Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.

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Provides authoritative guidance on Amazon Neptune Database and Neptune Analytics for graph, knowledge-graph, and relationship-heavy workloads — fraud detection / fraud rings, agentic memory / chatbot context across sessions, recommendations, identity resolution, Gremlin / openCypher / SPARQL queries, supernode / slow traversal, Neo4j to Neptune migration / APOC compatibility, Neptune Database vs Analytics engine selection, PageRank / community detection, GraphRAG, and connectivity from Lambda / EC2 / applications. Creates and modifies Neptune Database clusters/instances and Neptune Analytics graphs on explicit user confirmation; blocks destructive operations (delete, reset-graph, failover, major upgrade) and redirects to change-control.

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Use whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.

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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.

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Foundry IQ knowledge bases. WHEN: make local or Blob documents searchable; create/diagnose KBs; triage unsupported connectors or multi-source KB creation/reconfiguration (even with classic-search terms); connect existing KB to agents (including multi-source KBs); create/reuse a Search service; retrieve from an existing knowledge base with citations; no brand words needed. Read its procedure before query/target questions. NOT: other KB providers, repository-file search, classic Azure AI Search index/query/app work with no Foundry IQ KB or connector request, generic agent creation.

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Build, refresh and query a deterministic code knowledge graph to cut orientation-token cost. Triggers: knowledge graph, graphify, code graph, god nodes, orientation cost, map the codebase, what connects, callers of, blast radius.

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Maintain and update product knowledge base from releases, features, and project changes with optional wiki sync

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管理本地 Markdown/Obsidian 知识库：素材入库、健康检查、增量索引、关键词与 semantic-lite 检索、逐字原文资料包、归档和 MCP 连接。用于搜索知识库、整理资料及为 Agent 准备可定位的引用。

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When the user wants to optimize for entity recognition, Knowledge Graph, or entity-based SEO. Also use when the user mentions "entity SEO," "entity optimization," "Knowledge Graph," "Knowledge Panel," "entity signals," "brand entity," "entity linking," "entity relationships," or "entity-first content." For structured data, use schema-markup.

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Confluence expert for spaces, knowledge bases, documentation, page layouts, macros, templates, and Jira-Confluence integration. Use for documentation strategy, space architecture, content organization, and collaborative knowledge management.

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Expert knowledge for Azure SQL Database development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Hyperscale, serverless, geo-replication, Always Encrypted, or CI/CD/Terraform deployments, and other Azure SQL Database related development tasks. Not for Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db), Azure Synapse Analytics (use azure-synapse-analytics).

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Expert knowledge for Azure Role-based access control development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when defining custom roles, ABAC conditions, deny assignments, PIM-eligible roles, or ARM/Bicep-based role assignments, and other Azure Role-based access control related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Manager (use azure-resource-manager), Azure Security (use azure-security), Azure Lighthouse (use azure-lighthouse).

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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 configuring Azure Migrate appliances, Private Link, Arc/Resource Mover, VMware/Hyper-V discovery, or REST-based migrations, 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 Backup (use azure-backup).

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