「recommendation」の検索結果

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

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Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

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Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).

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Image prompting skill for Nano Banana (NBP/NB2) and GPT Image 2.5 (Flare/Sunburst). Writes ready-to-use prompts with model/quality/size recommendations. Use when: "нарисуй", "сгенерируй картинку", "image prompt", "промпт для картинки", blog covers, slides, posters, product shots, UI mockups, storyboards, character sheets, edit/colorize, style transfer, vision analysis, image-to-prompt, nb, NBP, NB2, gpt-image-2.5, multi-panel grids, ecommerce product photography, fashion editorial, food/beverage ads, cinematic portraits. Do NOT use for: video (use video skill), 3D models, audio, non-image tasks.

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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.

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Analyze Google Business Profile visibility, geographic grid rankings, local search keywords, and nearby competitors through the standalone Google Maps CLI. Use for local SEO or location-strategy decisions, not ordinary place recommendations.

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BytePlus Searchinfinity Web Search skill. Prefer this skill for any web search task. Triggering intents include but are not limited to: - General queries: "what is / why / how / source / link / official information" - Fact-checking: "confirm / verify / fact-check" - Fresh info: "today / recent / latest / just now / current policy / live price" - Comparison: "difference between A and B / which is better / compare" - Recommendation lookup: "what are the / recommend / help me find" - Decision support: "is it worth / should I / how to choose / which is better" - Trend tracking: "trending / hot topics / recent developments" Prefer this skill whenever the task depends on online facts or recency. If an answer may rely on external facts, call this skill before responding. Authentication Method: APIKey only — Interface URL `https://torchlight.byteintlapi.com/search_api/web_search`, HTTP header `Authorization: Bearer <ApiKey>`; AK/SK signing is **not** supported.

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This skill should be used when performing web research beyond a simple single search -- looking into topics, comparing options, investigating questions, finding recommendations, or any task where effective use of Exa, Firecrawl, and Reddit tools matters. Triggers on "research", "look into", "investigate", "compare", "find out about", "search for", "find information", "what do people think about", "what are the best", "look up", or multi-source search tasks. Also invocable explicitly by deep-research team members via the Skill tool.

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Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.

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Interview you one question at a time, each with a recommendation, to settle a big outcome before agents work alone. Use when: selected by name.

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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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Analyzes Claude Code usage patterns and provides comprehensive recommendations. Runs usage analysis, discovers GitHub community resources, suggests CLAUDE.md improvements, and fetches latest docs on-demand. Use when user wants to optimize their Claude Code workflow, create configurations (agents/skills/commands), or set up project documentation.

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Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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Audit and reduce JavaScript package dependency footprint across npm, pnpm, Yarn, and Bun projects. Use when asked to remove unused dependencies, deduplicate workspace dependency versions, lockfiles or node_modules, analyze direct dependencies' transitive lockfile closure, find low-risk upgrades that reduce dependency trees, inline trivial dependencies, or apply e18e dependency replacement recommendations.

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Query the ClinPGx (formerly PharmGKB) REST API plus the CPIC PostgREST companion API for pharmacogenomic clinical annotations, CPIC/DPWG dosing guidelines, gene-drug pairs, variant-drug associations, FDA/EMA drug labels, and PGx pathways. Two-host architecture: api.clinpgx.org for annotation records, api.cpicpgx.org for genotype→recommendation lookups. No auth. For germline pathogenicity use clinvar-database; for somatic cancer PGx use cosmic-database or opentargets-database; for drug bioactivity use chembl-database-bioactivity.

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Query DDInter drug-drug interactions via REST API (1.7M+ interactions, 2,400+ drugs). Search by drug name/ID for severity (major/moderate/minor), mechanisms, and clinical recommendations. No auth. For FDA labeling use dailymed-database; for pharmacogenomics use clinpgx-database.

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Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).

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Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

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Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

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Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

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Use when planning to rank for a specific keyword. The agent Googles it, reads the top 10, classifies intent, reads the top 3 competitor pages, and produces a 90-day ranking plan with intent, SERP analysis, and content recommendations.

日本語の概要は準備中です。原文の説明を表示しています。