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Found 84 Skills
Minimal text embedding smoke test for Model Studio embedding models.
Official Google Search guidance for optimizing websites for generative AI features such as AI Overviews and AI Mode. Use when an AI agent needs to explain, audit, plan, or implement SEO work for Google AI Search visibility; evaluate AEO/GEO claims; advise on llms.txt, structured data, content quality, crawlability, JavaScript SEO, media SEO, ecommerce/local details, Merchant Center, Business Profile, or agent-friendly site readiness.
Configure search result boosting in GrepAI. Use this skill to prioritize certain paths and penalize others.
Use when reranking search candidates is needed with Alibaba Cloud Model Studio rerank models, including hybrid retrieval, top-k refinement, and multilingual relevance sorting.
Optimize a listing for Amazon's AI shopping assistants (Rufus on the web, Alexa+ on devices, and the COSMO ranking layer behind them). Rewrites bullets as question answers, completes the Attributes section, models voice query anatomy, and reinforces with review-language. Use when a user asks about Rufus, Alexa+, AI shopping, AI-driven search, COSMO, conversational shopping, question-style queries, voice shopping, voice search, smart speaker discovery, or conversational query optimization. Trigger phrases. "Rufus", "Alexa+", "AI shopping", "AI search", "COSMO", "conversational query", "question answering", "voice shopping", "Alexa search", "voice query", "smart speaker", "conversational shopping". Works with zero tools.
Use AliCloud Milvus (serverless) with PyMilvus to create collections, insert vectors, and run filtered similarity search. Optimized for Claude Code/Codex vector retrieval flows.
Advanced search options in GrepAI. Use this skill for JSON output, compact mode, and AI agent integration.
Use Parallel's parallel-cli to do live web search, URL extraction (clean markdown), deep research reports, bulk data enrichment (CSV/JSON), FindAll entity discovery, and web monitoring. Use when the user asks to look something up online, needs current sources/citations, provides URLs to read or summarise, requests deep/exhaustive research, wants to enrich a dataset with web-sourced fields, wants a list of entities (companies/people/places), or wants to monitor the web for changes over time.
Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Use when text embeddings are needed from Alibaba Cloud Model Studio models for semantic search, retrieval-augmented generation, clustering, or offline vectorization pipelines.
When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.