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Found 158 Skills
Full SEO audit: Google Search Console data + URL Inspection API + PageSpeed Insights API + technical crawl + keyword research + metadata audit + schema markup audit + search intent analysis + Core Web Vitals monitoring. Feeds real GSC data and PageSpeed metrics into AI to surface quick wins, diagnose traffic drops, find content gaps, identify metadata mismatches, detect schema gaps, monitor page performance, and produce an actionable 30-day plan. Use this skill whenever the user asks about SEO, search rankings, organic traffic, Google Search Console, keyword performance, traffic drops, content gaps, search visibility, technical SEO, meta tags, schema markup, structured data, URL indexing, keyword research, indexing issues, page speed, performance, Core Web Vitals, LCP, INP, CLS, or Lighthouse scores. Also trigger on: "why is my traffic down", "what keywords am I ranking for", "improve my rankings", "check my search console", "SEO audit", "analyze my SEO", "technical SEO", "meta tags", "indexing issues", "crawl errors", "content strategy", "keyword cannibalization", "search intent", "schema markup", "structured data", "URL inspection", "page speed", "performance score", "core web vitals", "lighthouse", or any organic search question. If in doubt, trigger. This skill handles everything from quick GSC checks to deep technical audits with performance monitoring.
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications
How to read data from the Sui network. Use when choosing or implementing a data access strategy — queries for on-chain state, indexing pipelines, historical lookups, event subscriptions, cross-chain reads, or off-chain blob storage. Covers the three live Sui APIs (gRPC, GraphQL RPC, deprecated JSON-RPC), the Archival Store, the General-Purpose Indexer, the `sui-indexer-alt` custom indexing framework, and Walrus for off-chain blobs.
Use when generating, updating, or organizing documentation (component/API docs, project indexes, diagrams, tutorials, learning paths) - provides structured workflows and references for docs generation, indexing, diagrams, and teaching.
Comprehensive skill for the `kb` CLI and the Karpathy Knowledge Base pattern. Covers the full KB lifecycle — topic scaffolding, multi-source ingestion (URLs, files, YouTube, bookmarks, codebases), wiki article compilation, cross-article querying with file-back, lint-and-heal passes, QMD indexing, and hybrid search. Also covers codebase-specific analysis via inspect commands for complexity, coupling, blast radius, dead code, circular dependencies, symbol/file lookups, backlinks, and code smells. Use when working with kb CLI commands, knowledge base workflows, code vault generation, code graph analysis, code metrics inspection, wiki compilation, or the ingest-compile-query-lint cycle. Do not use for general code review, linting, formatting, building Go projects, or writing application code.
Use this skill when designing database schemas for relational (SQL) or document (NoSQL) databases. Provides normalization guidelines, indexing strategies, migration patterns, and performance optimization techniques. Ensures scalable, maintainable, and performant data models.
Use and read this skill immediately if the user request is in any way related to SEO or a site's organic search or AI search presence. That includes site audits, rankings, keyword research, competitors, backlinks, click or traffic changes, indexing problems, crawling, redirects, sitemaps, metadata, structured data, Core Web Vitals, internal links, content opportunities, programmatic SEO, local search, Search Console, Google Analytics or Clicky questions, Google update impact, llms.txt, AI search visibility in ChatGPT, Claude, Perplexity, or Google AI Overviews, and client SEO reporting. Routes to evidence-backed local reports through the SEO CLI and MCP server.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
Use when the user asks to "check technical SEO"; audits crawlability, indexing, Core Web Vitals, robots.txt, sitemaps, canonicals, redirects, and migrations. Not for on-page tags or content — use on-page-seo-checker. 技术SEO/网站速度
MySQL development best practices for schema design, query optimization, and database administration
SQL query optimization and performance tuning
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.