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VuePress processes markdown through a Vue-powered SSG pipeline, generating HTML with client-side hydration for interactive components.","intents":["Build a searchable, multi-section documentation site from markdown without managing a CMS","Deploy content updates automatically via GitHub Actions without manual build steps","Organize parallel learning paths (Vibe Coding + AI Knowledge Base) in a single site structure"],"best_for":["technical educators creating multi-track learning platforms","open-source projects needing versioned documentation with community contributions","teams migrating from wiki-based knowledge to version-controlled markdown"],"limitations":["VuePress 1.9.10 is legacy (v2 available) — limited plugin ecosystem and slower build times for 1000+ pages","Static site generation means no real-time content updates without rebuild + redeploy","Client-side search indexing limits scalability for 10,000+ documents without external search service","Sidebar and navbar configuration requires manual TypeScript editing — no UI-based content management"],"requires":["Node.js 12+ (VuePress 1.9.10 compatibility)","npm or yarn package manager","GitHub repository with Actions enabled for CI/CD","VuePress 1.9.10 and dependencies in package.json"],"input_types":["markdown files (.md) with YAML frontmatter","TypeScript configuration files (.ts in .vuepress/)","Vue component files (.vue) for custom theme overrides"],"output_types":["static HTML files","CSS bundles","JavaScript bundles with Vue hydration","JSON search index"],"categories":["automation-workflow","static-site-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_1","uri":"capability://memory.knowledge.hierarchical.content.organization.with.dual.learning.paths","name":"hierarchical content organization with dual learning paths","description":"Organizes markdown content into two parallel directory hierarchies (Vibe Coding 零基础教程/ and AI/) that map to distinct user personas and learning objectives. 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Each path has its own progression model: Vibe Coding uses 6-stage progression for beginners; AI path segments into DeepSeek documentation, application scenarios, project tutorials, and industry news.","intents":["Serve absolute beginners a zero-foundation programming course without overwhelming them with advanced AI concepts","Provide technical practitioners direct access to DeepSeek deep-dives and application scenarios","Enable content reuse across paths while maintaining separate navigation contexts"],"best_for":["educational platforms serving mixed skill levels (beginners + practitioners)","AI knowledge bases needing to segment content by use case (theory + applications + tools)","open-source projects with both tutorial and reference documentation"],"limitations":["No dynamic audience detection — users must manually navigate to their learning path; no automatic routing based on skill level","Content duplication across paths requires manual synchronization — changes to shared concepts must be updated in multiple locations","Sidebar configuration is static TypeScript — no runtime path customization or personalization based on user progress","Search index treats both paths equally — no way to boost results for a user's selected learning path"],"requires":["Markdown files organized in parallel directory structures","TypeScript knowledge to modify .vuepress/sidebar.ts for path configuration","VuePress 1.9.10 with sidebar plugin support"],"input_types":["markdown files (.md) in hierarchical directories","YAML frontmatter in markdown for metadata (title, order, etc.)"],"output_types":["nested sidebar navigation trees","breadcrumb trails reflecting content hierarchy","URL paths preserving directory structure"],"categories":["memory-knowledge","content-organization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_10","uri":"capability://tool.use.integration.ai.tool.usage.guide.aggregation","name":"ai tool usage guide aggregation","description":"Aggregates tutorials and best practices for popular AI development tools (Cursor, Claude Code, TRAE, Lovable, Copilot) into a searchable reference organized by tool and use case. 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The architecture treats each tool as a first-class entity with dedicated documentation, enabling users to compare tools and find the best fit for their workflow.","intents":["Help developers quickly learn new AI development tools without reading official documentation","Enable comparison of different tools (Cursor vs Copilot vs Claude Code) for the same task","Provide productivity tips and best practices for each tool based on community experience"],"best_for":["developers evaluating which AI coding assistant to adopt","teams standardizing on specific tools and needing training materials","educators teaching AI-assisted development with multiple tool options"],"limitations":["Tool documentation is manually curated — no automatic sync with official tool updates, so tutorials may become outdated","Screenshots and keyboard shortcuts may differ across tool versions — no version tracking","No interactive tool exploration — users cannot try tools directly in the documentation","Tool comparisons are static — no dynamic benchmarking or performance metrics"],"requires":["markdown files documenting each tool's features and usage","screenshots and keyboard shortcut references","links to official tool documentation","VuePress search index for discoverability"],"input_types":["markdown tool documentation","screenshots and diagrams","keyboard shortcuts and workflow examples","links to official tool resources"],"output_types":["tool usage guides and tutorials","feature comparisons","productivity tips and best practices","keyboard shortcut references"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_11","uri":"capability://code.generation.editing.ai.framework.integration.tutorial.system","name":"ai framework integration tutorial system","description":"Provides structured tutorials for integrating AI capabilities into applications using popular frameworks (Spring AI, LangChain) with code examples, architecture patterns, and best practices. The system uses markdown files with embedded code snippets showing how to implement common patterns (RAG, agents, tool calling) in each framework. Content is organized by framework and pattern, with cross-references to concept documentation and project tutorials. The architecture treats each framework as a distinct integration path, enabling users to choose the framework matching their tech stack.","intents":["Help developers integrate AI into existing applications using their preferred framework","Enable comparison of how different frameworks (Spring AI vs LangChain) implement the same AI pattern","Provide working code examples that can be adapted for production use"],"best_for":["backend developers adding AI features to existing Spring Boot or Python applications","teams evaluating which AI framework to adopt for their tech stack","architects designing AI integration patterns for enterprise systems"],"limitations":["Framework tutorials are manually curated — no automatic sync with framework updates or API changes","Code examples may use outdated library versions — no dependency version tracking","No interactive code execution — users must copy examples and run locally","Framework comparisons are static — no dynamic benchmarking or performance metrics"],"requires":["markdown files documenting each framework's AI integration patterns","code examples and snippets for common patterns","links to official framework documentation","VuePress search index for discoverability"],"input_types":["markdown framework documentation","code examples and snippets","architecture diagrams","links to official framework resources"],"output_types":["framework integration tutorials","code examples and snippets","architecture patterns and diagrams","best practices and recommendations"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_12","uri":"capability://text.generation.language.ai.product.monetization.strategy.guide","name":"ai product monetization strategy guide","description":"Provides guidance on building and monetizing AI products, including business models, pricing strategies, go-to-market approaches, and case studies. The system uses markdown files documenting different monetization models (SaaS subscriptions, API usage-based pricing, freemium + premium tiers) with examples of successful AI products. Content includes financial projections, customer acquisition strategies, and common pitfalls to avoid. The architecture treats monetization as a distinct knowledge domain separate from technical tutorials, enabling non-technical founders to learn business strategy alongside developers learning technical implementation.","intents":["Help founders understand how to monetize AI products beyond the technical implementation","Provide case studies and examples of successful AI product business models","Enable teams to make informed decisions about pricing and go-to-market strategy"],"best_for":["founders building AI startups and needing business strategy guidance","technical teams adding monetization to open-source AI projects","investors evaluating AI product business models"],"limitations":["Monetization strategies are static case studies — no real-time market data or pricing benchmarks","No financial modeling tools — users must manually calculate projections based on examples","Strategies may not apply to all markets or customer segments — no personalization based on product type","No community contribution workflow — adding new strategies requires repository access"],"requires":["markdown files documenting monetization strategies and case studies","examples of successful AI products and their business models","financial projection templates and examples","VuePress search index for discoverability"],"input_types":["markdown strategy documentation","case studies and examples","financial projection templates","pricing strategy frameworks"],"output_types":["monetization strategy guides","case studies and examples","pricing strategy recommendations","go-to-market templates"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_2","uri":"capability://tool.use.integration.embedded.community.engagement.and.monetization.widget.system","name":"embedded community engagement and monetization widget system","description":"Injects interactive widgets (QR codes, call-to-action buttons, partner service links) into the page sidebar and footer via .vuepress/extraSideBar.ts and .vuepress/footer.ts configuration modules. 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The architecture separates content generation scripts (JavaScript in root) from deployment configuration (GitHub Actions YAML workflows).","intents":["Deploy documentation updates automatically when contributors push markdown changes to GitHub","Generate navigation metadata and search indexes without manual intervention","Maintain a single source of truth (GitHub repository) that automatically syncs to production site"],"best_for":["open-source projects with distributed contributors who lack deployment access","teams wanting to eliminate manual build-and-deploy steps for documentation","content-heavy sites needing frequent updates without downtime"],"limitations":["GitHub Actions free tier limits concurrent jobs — large builds (1000+ pages) may queue or timeout","Build artifacts are ephemeral — no built-in versioning or rollback mechanism if a bad commit breaks the site","Secrets management (API keys, deployment credentials) requires GitHub repository configuration — no local-first workflow","Build logs are GitHub-specific — integrating with external CI/CD systems requires webhook bridges"],"requires":["GitHub repository with Actions enabled","GitHub Actions workflow YAML files in .github/workflows/","Node.js 12+ and npm/yarn for running build scripts","Deployment credentials (SSH key, API token) stored as GitHub Secrets"],"input_types":["markdown files pushed to GitHub","TypeScript configuration changes in .vuepress/","JavaScript build script modifications"],"output_types":["static HTML/CSS/JS assets","deployed site at production URL","GitHub Actions workflow logs and status"],"categories":["automation-workflow","ci-cd"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_4","uri":"capability://memory.knowledge.multi.model.ai.tool.and.framework.tutorial.aggregation","name":"multi-model ai tool and framework tutorial aggregation","description":"Curates and organizes tutorials for multiple AI models (DeepSeek, GPT, Gemini, Claude) and frameworks (LangChain, Spring AI) into a searchable knowledge base. 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The architecture treats news as a distinct content type with its own directory, separate from tutorials and reference documentation.","intents":["Help developers stay informed about new AI model releases and framework updates without subscribing to multiple news sources","Enable historical analysis of AI trends by browsing news archives by month/year","Provide context for why certain AI tools or approaches became popular by linking to contemporary news"],"best_for":["AI practitioners who want a curated news feed without algorithm-driven social media","researchers tracking the pace of AI development over time","teams evaluating new tools and wanting to understand when they were released and adopted"],"limitations":["News is manually curated — no automated feed aggregation from RSS or news APIs, so coverage depends on curator attention","No real-time updates — news is published on a fixed schedule (monthly archives), not as events occur","No filtering or personalization — all users see the same news items regardless of their interests","Archive is append-only — no way to update or correct news items after publication"],"requires":["markdown files organized by month in AI/AI 行业资讯/ directory","consistent date formatting and metadata in frontmatter","VuePress search index for discoverability"],"input_types":["markdown news items with publication date and tags","links to external news sources","brief summaries of industry developments"],"output_types":["chronologically-sorted news archive","searchable news index","tagged news items for filtering"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_7","uri":"capability://memory.knowledge.prompt.engineering.and.technique.knowledge.base","name":"prompt engineering and technique knowledge base","description":"Aggregates prompt engineering techniques, prompt templates, and best practices (Prompt 提示词大全) into a searchable reference organized by use case and technique type. 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The architecture treats concepts as first-class entities with dedicated pages rather than scattered throughout tutorials.","intents":["Help developers understand foundational AI concepts without reading full tutorials","Enable quick lookup of concept definitions and relationships during implementation","Provide a learning path for understanding complex concepts by following cross-references"],"best_for":["developers new to AI who need quick concept definitions","teams building AI systems who need shared vocabulary and understanding","educators teaching AI concepts with clear definitions and relationships"],"limitations":["Concepts are static markdown — no interactive visualizations of concept relationships or knowledge graphs","No version tracking — unclear which concept definitions apply to which technology versions","Cross-references are manual links — no automatic detection of related concepts or suggestions for missing links","No community contribution workflow — adding new concepts requires repository access"],"requires":["markdown files for each concept with clear definitions","metadata tags for concept relationships and prerequisites","VuePress search index for discoverability"],"input_types":["concept definitions and explanations","architectural diagrams and examples","prerequisite and related concept metadata"],"output_types":["concept encyclopedia pages","cross-referenced concept links","concept relationship metadata"],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-liyupi--ai-guide__cap_9","uri":"capability://code.generation.editing.hands.on.ai.project.tutorial.library","name":"hands-on ai project tutorial library","description":"Curates step-by-step tutorials for building complete AI projects (AI/AI 项目教程/) with working code examples, architecture diagrams, and deployment instructions. 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