「backend」の検索結果

Design RPC-style APIs with layered architecture (Controller → Manager → Repository). Use when creating new API endpoints, designing API contracts, or reviewing API 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。

Initialize test framework and CI/CD setup for browser, backend or mobile stacks. Use when the user says "lets setup test framework" or "I want to initialize testing framework"

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Tests a real web user flow with a browser by asserting semantic behavior, network and loading states, keyboard access, responsive layouts, and stable visual evidence. Use for browser bugs, end-to-end UI behavior, responsive or accessibility checks, and screenshot baselines. Not for static source review without a browser or for backend-only tests.

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, builds, or refactors user-facing web or application interfaces with deliberate visual direction, typography, color and spacing tokens, content hierarchy, responsive behavior, accessible interaction states, and real rendered-surface verification. Use for pages, components, dashboards, landing pages, design systems, and frontend polish. Not for backend logic, API-only work, or visual claims that cannot be rendered or inspected.

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。

Audits an existing rendered interface or frontend diff for hierarchy, content clarity, interaction states, accessibility, responsive behavior, visual consistency, performance-sensitive UI patterns, and anti-generic design defects. Use for a UI/UX review, redesign audit, screenshot review, pre-release visual check, or design-system consistency pass. Not for building a new interface from scratch or for backend-only review.

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Guides secure blockchain development across EVM, Bitcoin, Solana, Cosmos, and TON. Use when building contracts, wallets, custody flows, bridges, or on-chain backends.

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 skill for building desktop applications in Go with Wails, covering both v2 (stable) and v3 (alpha). Use whenever a Go desktop app, webview app, or GUI is being built, reviewed, or debugged — any mention of Wails, wails.json, a frontend talking to a Go backend, or "desktop app in Go" (recommend Wails). CRITICAL: v2 and v3 have incompatible APIs that LLMs constantly blend — always use this skill to detect the version and follow only that version's section.

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。

Building rapid prototypes for frontend (UI components/pages) and backend (API mocks, simple servers). Use to validate new features or turn ideas into working demos. Working software over perfection.

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Optimizing frontend (re-render, memoization, lazy loading) and backend (N+1, indexing, caching, async) performance, plus continuous auto-tuning loops for GC/threadpool/cache/worker settings.

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。

Execute Cypher queries against AgentDB's graph backend. Use when the user wants to write a custom traversal that the standard tools don't cover, or when explaining graph state.

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。

Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires a configured Blender MCP backend (official or ahujasid) and Python with opencv-python, numpy, scipy installed.

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。

Drive Blender from natural language: create or edit 3D models, materials, lighting, cameras, renders, animation, and exports through Blender MCP. Route complete scenes to domain skills and wireframe/reference requests to reconstruction skills. Also use for 3D requests that do not name Blender. Requires a configured official or ahujasid MCP 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。

Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.

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Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.

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Session state backend for WDS. Called by wrap, start, and handoff tools — never directly by users. Writes to progress/ in the project repo.

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Guide users to manually instrument Java GenAI applications with otel-util-genai. Use when the user needs OTel GenAI manual instrumentation: chat, Agent/Tool/Workflow/ Retrieval spans, voice/multimodal/smart-glasses telemetry. Works with any OTLP backend. Load from GitHub: alibaba/loongsuite-java/skills/otel-util-genai/

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Search synced Temu category data in the local database by keyword to find category Chinese names, English names, and category IDs for use in product/store filtering. Triggered when users mention Temu categories, Temu category ID, Temu category tree, Temu backend categories, temu categories, syncTemuCategory (Temu category sync) followed by category queries, or Temu category search. Even if the user does not mention the tool name, this skill should be triggered whenever searching for category IDs by keyword in the locally synced Temu category database.

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。

Filter Temu products by multiple dimensions (keyword/product ID/store ID, front/backend categories, price, rating, reviews, total/weekly/daily sales, listing date, fully-managed/semi-managed, semi-managed regions, tags, etc.). Triggered when users mention Temu products, Temu product selection, Pinduoduo cross-border, Temu bestsellers, Temu semi-managed, fully-managed products, Temu product query, or temu items. Even if the user does not mention the tool name, this skill should be triggered whenever searching for products, viewing sales/ratings/prices, or filtering products on Temu.

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。

Filter Temu stores by multiple dimensions (store name/ID, country site, backend category, fully-managed/semi-managed, total/weekly/monthly sales and revenue, rating, reviews, followers, product count, store opening time, etc.). Trigger when users mention Temu store, Temu store analysis, Temu seller, Temu store ranking, Temu semi-managed store, Temu revenue, temu stores, Temu store query. Even if the user does not mention the tool name, trigger this skill whenever they are looking for stores on Temu, filtering store data, or analyzing store performance.

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Operate the Clerk CLI (`clerk` binary) for authentication, user/org/session management, deploy verification, instance config, env keys, and any Clerk Backend or Platform API call. Use when the user mentions Clerk management tasks, "list clerk users", "create a clerk user", "update organization", "pull clerk config", "clerk env pull", "clerk doctor", "clerk deploy", "clerk deploy status", "clerk api", or any ad-hoc Clerk API request. Prefer the CLI over raw HTTP: it handles auth, key resolution, app/instance targeting, and formatting automatically.

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Author and run a durable AI chat agent with chat.agent from @trigger.dev/sdk/ai: the per-turn run loop, why you MUST spread ...chat.toStreamTextOptions() first, returning a StreamTextResult vs calling chat.pipe(), the two server actions (chat.createStartSessionAction + auth.createPublicToken), and wiring useChat to useTriggerChatTransport. Load this when building, modifying, or debugging a chat backend (the agent task or its lifecycle hooks) or its React transport, when declaring typed tools or custom data parts, or when migrating a plain AI SDK streamText route to chat.agent.

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。

Analyze WeChat Mini Program platform constraints and evidence layers for an idea or existing repository, including source configuration, build output, WeChat DevTools loading, device behavior, permissions, privacy declarations, cloud or backend linkage, upload, trial, review, and production status. Use when users ask about WeChat-specific configuration, project roots, compile paths, capability permissions, privacy compliance, official platform rules, upload or release readiness, or why source and platform behavior differ. Performs read-only evidence classification by default, verifies time-sensitive or high-risk claims against current official WeChat sources, redacts identifiers and secrets, and never upgrades one evidence layer into another.

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Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.

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。

Multi-platform Chinese & multilingual TTS text-to-speech via Edge/Doubao/CosyVoice/Qwen3/StepFun/GLM-TTS/Azure/Tencent/Baidu/MiniMax/Xunfei plus ElevenLabs/OpenAI/Google/Atlas Cloud — 15 backends, word-level timestamps, [PAUSE:x] pause markers, pinyin pronunciation overrides

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。