Data analysis, machine learning, LLM apps, and prompt design (9K skills)
Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
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Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
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Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
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Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.
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Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
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Provides patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.
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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
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Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
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Use when implementing caching, session storage, rate limiting, or any Redis integration. Covers cache-aside pattern, key naming, TTL strategy, and serialization config.
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Use when generating JPA entities, repositories, queries, or anything touching the persistence layer. Covers entity conventions, N+1 prevention, projections, and query patterns.
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Use when creating database migrations, schema changes, seed data, or any SQL that modifies database structure. Covers Flyway naming conventions, versioning, and safe migration patterns.
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Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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Inventory staged local EcoSIM data at project startup and validate input readiness for a selected site, simulation period, and preprocessing or analysis workflow. Use before data-dependent work or when relevant inputs change.
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Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing data.
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Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.
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画像・動画プロンプトの新規作成や意味変更で、参照素材・空間・構図・動作を設計する。誤字や生成内容を変えない形式修正には使わない。
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Write or revise MiniMax H3 prompts for text, keyframe, and full-reference modes, preserving mode-specific fields, labels, and timing.
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商品・店舗情報と参照素材から、MiniMax H3用の縦型リール広告を企画し、生成プロンプトと編集設計を作成・監査する。生成や広告配信は行わない。
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MiniMax H3用の文字・図形・商品を主役にしたモーショングラフィックスプロンプトを作成・監査する。生成実行は別スキルで扱う。
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Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific system.
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Import a memory export from another AI assistant into Claude's memory — conversationally, additively, and with the content treated as data.
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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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