297 件 ・ 関連度順
概要と使いどころ
Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
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。
Searches and navigates stored knowledge in memory palaces. Use when looking for previously stored information or cross-referencing concepts across palaces.
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。
SwiftData patterns — autosave, relationships, dangerous predicates, CloudKit constraints, indexing, class inheritance. Use when writing, reviewing, or debugging SwiftData code.
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。
Guides Gensim topic modelling, document indexing, embeddings, similarity retrieval, downloader, and script workflows for Python NLP and IR tasks.
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。
Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning
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。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.
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。
Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.
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。
Use when an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.
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。
Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
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。
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
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。
Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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。
Use when tutoring a learner through MATLAB debugging, error interpretation, failed tests, incorrect outputs, array-shape problems, indexing mistakes, function argument issues, or code repair practice. Use for guided debugging sessions, debugging drills, teach-the-agent critique, and evidence-based MATLAB troubleshooting.
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。
Designs database schemas with proper normalization, indexing, constraints, and tenant isolation patterns. Use when someone needs to create a new database schema, add multi-tenant support, design row-level security policies, or optimize table structures. Trigger words: database schema, table design, RLS, row-level security, foreign keys, indexes, migrations, ERD, data model, normalization.
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。
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
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。
Algolia Search Integration workflow skill. Use this skill when the user needs Expert patterns for Algolia search implementation, indexing and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Algolia Search Integration workflow skill. Use this skill when the user needs Expert patterns for Algolia search implementation, indexing and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Algolia Search Integration workflow skill. Use this skill when the user needs Expert patterns for Algolia search implementation, indexing and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
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。
Google API integration for blog performance: PageSpeed Insights, CrUX Core Web Vitals with 25-week history, Search Console performance, URL Inspection, Indexing API, GA4 organic traffic, NLP entity analysis for E-E-A-T, YouTube video search for embedding, and Google Ads Keyword Planner. Progressive feature availability based on credential tier (API key, OAuth/service account, GA4, Ads). Shares config with claude-seo at ~/.config/claude-seo/google-api.json. Use when user says "google data", "page speed", "core web vitals", "search console", "indexation", "GA4", "keyword research", "nlp entities", "blog performance", "youtube search", "google api setup".
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。
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Use when writing or changing a query against PostgreSQL from Go — avoiding N+1 loops, reading EXPLAIN output, indexing, and keyset pagination
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。
Use when indexing a code repository into Cognee's code graph or querying it (callers, impact analysis, paths, endpoints) from Codex.
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。