「client」の検索結果

Use when adding cross-cutting concerns to durable functions — structured logging or tracing across all functions, error tracking with Sentry, payload encryption for sensitive data, dependency injection of clients (DB, Stripe, etc.) into function handlers, custom telemetry, or behavior that should apply uniformly across many functions. Covers Inngest middleware lifecycle, creating custom middleware, dependencyInjectionMiddleware, @inngest/middleware-encryption, @inngest/middleware-sentry, and custom middleware patterns.

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Use when adding durable execution to a TypeScript project — building retry-safe webhook handlers, background jobs that survive crashes, scheduled tasks, or long-running workflows that outlive a single request. Covers Inngest SDK installation, client config, environment variables, serve endpoints (Next.js, Express, Hono, Fastify), connect-as-worker mode, and the local dev server.

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 streaming durable workflow updates to a UI in real time — live order status pages that animate as steps complete, AI agent token streaming from a function to the browser, log tailing for long-running jobs, or human-in-the-loop approval flows that publish a prompt and wait for a user reply. Covers Inngest v4 native realtime: defining typed channels, publishing from inside step.run, minting subscription tokens via server actions, and consuming the stream from React/Next.js client components.

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 upgrading an existing TypeScript codebase from Inngest SDK v3 to v4, or when fixing mixed v3/v4 API usage. Covers detecting current SDK usage, moving triggers into createFunction options, replacing EventSchemas with eventType/staticSchema, moving serve options to the client, updating realtime imports, rewriting step.invoke string IDs, checkpointing/serverless runtime settings, Connect option changes, and verification.

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。

Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.

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Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHIF/Slim links. Use pydicom-medical-imaging for local DICOM reading; histolab for whole-slide pathology preprocessing.

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 Norma to coordinate architecture, buildings, projects, clients, proposals, schedules, specifications, sites, zoning, FF&E and firm work through the relevant Arch Studio owners. Direct skill requests stay with their owner.

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。

Provides Next.js App Router data fetching patterns including SWR and React Query integration, parallel data fetching, Incremental Static Regeneration (ISR), revalidation strategies, and error boundaries. Use when implementing data fetching in Next.js applications, choosing between server and client fetching, setting up caching strategies, or handling loading and error states.

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Provides patterns for unit testing external REST APIs using WireMock. Stubs API responses, verifies request details, simulates failures (timeouts, 4xx/5xx errors), and validates HTTP client behavior without real network calls. Use when testing service integrations with external APIs or mocking HTTP endpoints.

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。

Provides comprehensive code review capability for Next.js applications, validates Server Components, Client Components, Server Actions, caching strategies, metadata, API routes, middleware, and performance patterns. Use when reviewing Next.js App Router code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review Next.js code", "Next.js code review", "check my Next.js app".

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 Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.

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。

Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.

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Provides Spring Boot MCP server patterns that create Model Context Protocol servers with Spring AI by defining tool handlers, exposing resources, configuring prompt templates, and setting up transports for AI function calling and tool calling. Use when building MCP servers to extend AI capabilities with Spring's official AI framework, implementing AI tools, custom function calling, or MCP client integration.

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Builds estimates, quotes, and bids for trades and field-service businesses (HVAC, plumbing, electrical, roofing, landscaping, painting, cleaning, remodeling, general contracting, IT/AV installs). Turns a scope into line items with waste factors, burdened labor, materials, equipment, subs, permits, mobilization, contingency, overhead, profit, and tax, then writes a client-ready xlsx (live formulas) and PDF plus an internal cost sheet, with good/better/best options, add-ons, a payment schedule, exclusions, and assumptions. Use whenever someone asks for a quote, estimate, bid, proposal price, or job price, asks "how much should I charge for this job", wants to check a price or margin on a job, or is confused about markup vs margin. Computes deterministically with a script instead of mental math, because models routinely treat a 30% markup as a 30% margin and drop labor burden, waste, and mobilization.

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Builds and edits PowerPoint decks bound to a company's own .pptx or .potx template, with a consulting-grade storyline and a rendered visual QA pass. Maps the template's masters, layouts, placeholders, theme colors and fonts; drafts a storyboard of action titles (pyramid principle, SCR) for approval; fills the template's own placeholders instead of drawing text boxes; adds native charts in theme colors, tables sized to placeholders, speaker notes and source lines; then renders every slide to PNG and checks text overflow, off-slide and overlapping shapes, off-theme fonts and colors, empty placeholders and weak titles. Use whenever the user says use our template, company deck, brand or corporate template, make slides, build a deck, pptx, potx, consulting-style deck, board or client presentation, action titles, or fix my deck formatting, and whenever an existing deck must be edited without breaking its masters. Complements the generic pptx skill.

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Predicts client renewal likelihood based on health score signals. Analyzes engagement, support, adoption, satisfaction, billing, stakeholder, and usage data to calculate a weighted Health Score (0-100), classify renewal risk, and generate a prioritized action plan with early warning signals and recommended save plays.

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。

Audit a codebase for LLM calls that are really classifications in disguise, then produce a costed swap plan for a System One model. Use when asked to cut AI inference cost or latency, when scoping a performance engagement for a client, when reviewing an agent loop that feels slow, or when asked "where could we use Jev here". Produces a ranked table of candidates with measured latency and dollar deltas.

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Generates customized client onboarding checklists with phased tasks, ownership assignments, dependencies, acceptance criteria, and email templates. Adapts to consulting, SaaS, or agency engagement models.

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。

Point Cowork at your invoices and payments records -- builds an AR aging report, drafts escalating follow-up emails matched to each invoice's age and the client relationship, and runs as a weekly scheduled task so nothing slips past 30 days unnoticed.

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Cowork-style cleanup of a messy folder or shared drive -- classifies every file, proposes a target structure, renames to a consistent convention, deduplicates, and writes an index. Use on Downloads, Desktop, client folders, or team shared drives.

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 Galaxy repository guidance for server configuration, APIs, tool and workflow development, data/storage, Tool Shed operations, and web-client 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。

Guide the official Elasticsearch Python client for cluster connections, generated APIs, async code, bulk ingestion, query DSL, ES|QL, optional integrations, and runtime 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。

Use DB-GPT 0.8.1 as an agentic AI data platform: install and configure provider profiles, operate its CLI and services, build agents and AWEL flows, ingest and retrieve data with RAG, call client/API surfaces, and execute code through explicitly bounded sandbox runtimes.

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 CLIP-as-service client, server, and CLIP search workflows for text/image embeddings, cross-modal ranking, and retrieval services.

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。