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Found 104 Skills
Extract structured data from 40+ websites including Amazon, LinkedIn, Instagram, TikTok, Facebook, YouTube, and more. Uses Bright Data's Web Data APIs with automatic polling. Returns clean JSON with product details, profiles, reviews, posts, and comments.
Generate complete SEO setup for local business websites — HTML head tags, JSON-LD LocalBusiness schema, robots.txt, sitemap.xml. Australian-optimised with +61 phone, ABN, suburb patterns.
This skill should be used when users need to work with the Vercel AI SDK for building AI-powered applications. It provides comprehensive guidance on core APIs (generateText, streamText), UI components (useChat, useCompletion), tool calling, structured data generation, provider management, streaming protocols, and advanced features like middleware and custom providers.
When the user wants to add or optimize structured data (Schema.org, JSON-LD). Also use when the user mentions "schema," "structured data," "JSON-LD," "rich results," "rich snippets," "Google rich snippets," "featured snippet schema," "add schema to page," "missing structured data," "schema validation error," "Schema Markup Validator," "Google Rich Results Test," "FAQ schema," "Article schema," "Organization schema," "JobPosting," "HowTo," "Event," "SoftwareApplication," "BreadcrumbList," "WebSite," "Recipe," "Product," "Dataset," or "GEO."
Use this skill when implementing structured data markup using JSON-LD and Schema.org vocabulary for rich search results. Triggers on adding schema markup for FAQ, HowTo, Product, Article, Breadcrumb, Organization, LocalBusiness, Event, Recipe, or any Schema.org type. Covers JSON-LD implementation, Google Rich Results eligibility, validation testing, and framework integration (Next.js, Nuxt, Astro).
Parse current CNKI search results page into structured paper data (title, authors, journal, date, citations). Use after a search has been performed and you need to extract the results.
Generate JSON-LD structured data markup for rich results in Google Search. Supports FAQ, HowTo, Article, Product, LocalBusiness, and multi-type schemas. Validates against Google requirements and provides implementation guidance. Use when asked to "add schema markup", "generate structured data", "JSON-LD", "rich snippets", "FAQ schema", "product markup", "add structured data to my page", "how to get rich snippets", or any structured data task.
Use when diagnosing crawl/index issues, performance regressions, or structured data gaps.
Firecrawl v2.5 API for web scraping/crawling to LLM-ready markdown. Use for site extraction, dynamic content, or encountering JavaScript rendering, bot detection, content loading errors.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Extract structured advertising campaign parameters from natural language input provided by advertisers. This skill should be used when analyzing advertising requirements, campaign briefs, or ad requests that need to be converted into structured data. Supports both creating new campaigns and updating existing campaigns with additional information. Identifies missing information and provides helpful guidance for completing campaign requirements.
Structured data extraction from web pages using claude-in-chrome MCP with sequential-thinking planning. Focus on READ operations, data transformation, and pagination handling for multi-page extraction.