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Test LangBot through its WebUI and compare visible results with backend logs or network behavior. Use it to verify chat, model settings, bots, knowledge bases, and plugins, with recorded evidence for test results.
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Build a lasting research knowledge base linking papers, ideas, experiments, and claims. Keep failed approaches and unresolved gaps available for future work, generate compact summaries, and check records for missing connections or conflicting evidence.
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Design and implement RAG systems, which retrieve documents to inform AI answers. Get document preparation strategies, vector database comparisons, combined keyword and meaning-based search, result reranking, and an evaluation plan for retrieval quality.
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Routes requests about a codebase to the appropriate skill for mapping its structure, generating knowledge graphs, updating documentation, extracting lessons, or storing and recalling project knowledge for future planning.
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Search configured Yuxi knowledge bases to answer questions using internal or uploaded documents. Open source text, locate relevant passages, inspect mind maps, and identify supporting files so answers can be traced back to their evidence.
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Test whether instructions planted in an agent's long-term memory or searchable knowledge base influence later behavior. Verify both a stored test marker and a resulting change in answers, permissions, or treatment of retrieved documents.
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Extract a YouTube video’s full transcript and metadata, meaning details such as its title, channel, and like count, through the BrowserAct API. Use the results as source material for summaries, content research, or knowledge bases.
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Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
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[omh] Scattered knowledge needing a wiki: wiki construction blueprints and retained knowledge capture with destination-aware external knowledge connection guidance. Use when the user says: wiki, project wiki, build a wiki, start a wiki, organize my notes, external knowledge store, knowledge base, Obsidian.
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Expert knowledge for Azure Database Migration Service development including troubleshooting, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when planning Azure DMS migrations for MySQL, PostgreSQL, MongoDB, SSIS-to-Azure SQL, or hybrid workloads, and other Azure Database Migration Service related development tasks. Not for Azure Migrate (use azure-migrate), 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).
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Load project-specific context -- tech stack, architecture overview, directory layout, trusted sources, and conventions -- so that all skills operate with awareness of what this project actually is. Use when a knowledge base document exists, or when the user asks about the project's tech stack, architecture, conventions, framework, directory layout, or says 'tell me about this project', 'what are we using?', 'what's our stack?', or 'what framework is this?'. Use the knowledge-priming-refiner to create a knowledge base document.
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Initialise a project for knowledge base use. Appends the [Knowledge Base] section to the project's CLAUDE.md, creates the library/ directory structure, optionally seeds with three example library files from the Agentic SDLC research, and reports next steps. Run once after installing sdlc-knowledge-base.
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Build AI knowledge base creation systems. TRIGGERS - Use when user needs help with ai-knowledge-base-builder related tasks.
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A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern with a structured knowledge graph. Ingest documents once — the LLM writes wiki pages, extracts entities/relations into the graph, and stores raw content for evidence retrieval. Knowledge accumulates and cross-references; it is never re-derived from scratch. Use when building a knowledge base from many documents that should accumulate and cross-reference over time instead of being re-read each session.
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Build AI knowledge base creation systems. TRIGGERS - Use when user needs help with ai-knowledge-base-builder related tasks.
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Review database schema design and detect anti-patterns. Based on common RDB design knowledge and best practices. Supports Prisma, Drizzle, Rails, Django, Laravel, TypeORM, Sequelize, SQLAlchemy, and raw SQL (DDL). Use when the user says "review DB design", "check schema", "review table design", "review migration", "check for anti-patterns", "DB設計をレビューして", "スキーマを見て", "テーブル設計を確認して", "マイグレーションをレビューして", "アンチパターンがないか調べて", "この設計で問題ないか見て".
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Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
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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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Ask questions about a codebase using a knowledge graph, a map of its components and relationships. Get explanations tied to specific files, functions, dependencies, and architectural layers, with warnings when the graph may be outdated.
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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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Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
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Design knowledge systems that preserve document structure and connections across sources. Choose retrieval methods, organize conversation memory, and plan evidence-based updates, review workflows, and access controls for information that scattered text searches cannot adequately cover.
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Retrieve facts from named scientific, regulatory, economic, and other databases through documented APIs. Get results with recorded filters, retrieval dates, and record counts so another person can repeat the lookup and assess its completeness.
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Build a lasting Markdown knowledge base for project decisions, architecture, and session discoveries. Search pages by keywords and tags, use results to answer with citations, and check for outdated content, broken links, and structural problems.
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