「pipelines」の検索結果

Modern Java idioms for any JVM project (Java 17+): records, sealed types, text blocks, pattern matching, Optional discipline, streams, immutability, null safety, package layout, var usage, and code organisation. Apply when the project is a Java 17+ project. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Write self-documenting, immutable-by-default value types with records. - Model closed type hierarchies with sealed classes and interfaces. - Handle optionality explicitly with Optional instead of returning null. - Write expressive pipelines with Stream API without performance footguns. - Lay out packages consistently (feature-first or layer-first) and keep class responsibilities tight. Do NOT use this skill for: - Framework-specific idioms (Spring annotations, JPA mappings — those live in framework plugin skills). - Build tooling (Maven/Gradle) — see java-foundation:build-tooling. - Testing patterns — see java-foundation:jvm-testing.

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Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

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Use when explaining branch predictors, mispredict penalties, speculative execution, Spectre or Meltdown mitigations, or branchless code. Not for pipeline stage theory: use cpu-pipelines-and-hazards.

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Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.

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Use when setting up or modifying CI/CD pipelines, quality gates, test runners, or deployment pipeline configuration through workflow files. Not for triggering a deployment.

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Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.

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Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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Use when working with TeamCity CI/CD or when a user provides a TeamCity build URL — drives the `teamcity` CLI for builds, logs, jobs, queues, agents, pools, projects, and pipelines.

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Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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Build AI image processing pipelines for generation and analysis. TRIGGERS - Use when user needs help with ai-image-pipeline related tasks.

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Build AI orchestration pipelines with routing. TRIGGERS - Use when user needs help with ai-orchestration-pipeline related tasks.

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Build AI data extraction pipelines. TRIGGERS - Use when user needs help with ai-extraction-pipeline related tasks.

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Build DPO training pipelines for AI alignment. TRIGGERS - Use when user needs help with ai-dpo-trainer related tasks.

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Build data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.

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Design document processing pipelines for extraction and routing. TRIGGERS - Use when user needs help with ai-document-processor related tasks.

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Build AI audio processing pipelines. TRIGGERS - Use when user needs help with ai-audio-processor related tasks.

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Build Solana applications with Helius infrastructure. Covers transaction sending (Sender), asset/NFT queries (DAS API), real-time streaming (WebSockets, Laserstream), event pipelines (webhooks), priority fees, wallet analysis, and agent onboarding.

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Complete toolkit for programmatic video creation with Remotion + React. Covers animations, timing, rendering (CLI/Node.js/Lambda/Cloud Run), captions, 3D, charts, text effects, transitions, and media handling. Use when writing Remotion code, building video generation pipelines, or creating data-driven video templates.

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Scaffold Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.

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Use when the user asks to create, edit, rebuild, or refine editable diagrams in `.drawio` / diagrams.net format, especially for academic figures such as system architecture diagrams, technical roadmaps, workflow diagrams, method pipelines, module relationship diagrams, and experiment flowcharts. Use this skill when the user wants publication-ready outputs with both an editable source file and a PNG export, or when an existing figure image needs to be rebuilt into a clean editable academic-style diagram.

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Use when the user asks about initial access brokers (IABs), the access-listing market, ransomware-feeding-IAB pipelines, specific broker handles, or how access is priced and packaged. Self-updating knowledge cell.

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Build modern full-stack web applications with Next.js (App Router, Server Components, RSC, PPR, SSR, SSG, ISR), Turborepo (monorepo management, task pipelines, remote caching, parallel execution), and RemixIcon (3100+ SVG icons in outlined/filled styles). Use when creating React applications, implementing server-side rendering, setting up monorepos with multiple packages, optimizing build performance and caching strategies, adding icon libraries, managing shared dependencies, or working with TypeScript full-stack projects.

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Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native applications.

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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。