「serve」の検索結果

GraphQL expert including schema design, Apollo Client/Server, and caching

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。

Applies general rules for Convex development, emphasizing schema design, validator usage, index-first query patterns, function registration, and correct handling of system fields.

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。

Search and retrieve academic papers from arXiv.org using WebFetch and Exa. No MCP server required - uses existing tools to access arXiv API directly.

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 Cloud Run deployment, service management, traffic splitting, and log inspection. Use when deploying containerized apps to Cloud Run, managing services, or inspecting Cloud Run logs.

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。

用户需要从外部可观测信号推断内部隐藏状态时; 当需要"从表象看本质"时; 当直接观测不可行, 必须通过间接指标推断时。 不适用于: 可以直接观测内部的场景、纯数据查询、不需要推断的场景。

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。

Search academic papers, build literature reviews, and synthesize research findings — combines Exa MCP (research_paper category, arxiv filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Triggers on academic paper, literature review, research synthesis, arxiv, find papers, scholarly search.

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。

Configure and manage Supabase projects using MCP (Model Context Protocol). Use this skill when working with Supabase databases, setting up MCP servers, designing database schemas, implementing Row Level Security, managing migrations, or building modern data architectures with PostgreSQL. Essential for Supabase development, database design, and AI-powered database operations.

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。

Stealth headless browser with CDP server for anti-fingerprint browsing, parallel scraping, and low-memory CDP automation. This skill should be used when stealth browsing, anti-detection scraping, headless CDP automation with fingerprint protection, or tracker-free page fetching is needed. Triggers on 'stealth browse', 'anti-fingerprint', 'obscura', 'stealth CDP', 'undetectable scraping', 'headless stealth', 'tracker-free browsing'.

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 Figma designs into production-ready code using MCP server tools. Use this skill when users provide Figma URLs, request design-to-code conversion, ask to implement Figma mockups, or need to extract design tokens and system values from Figma files. Works with frames, components, and entire design files to generate HTML, CSS, React, or other frontend 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。

This skill should be used to search, inspect, list, and recover local Codex sessions through Codex's native App Server APIs. Trigger for requests such as find a prior Codex conversation, search Codex transcript text, list recent Codex work, locate matches inside a session, or produce a safe Codex resume command. Do not use for Claude Code sessions, project source-code search, or web search.

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。

Search, post, monitor, and archive on Twitter/X via five tools: the official hosted X MCP server (full-archive search, trends, bookmarks, Articles via xurl bridge), x-search CLI (cost-tracked API v2 research with calendar-day windows, volume counts, multi-query fan-out, feeds, watchlists, posting), xurl (official CLI for any endpoint incl. media/DMs), bird CLI (free session-based reads/writes, frozen at 0.8.0), and Smaug bookmark archival. Triggers on: search tweets, tweets from today, what did X tweet, latest tweets from, tweet volume, expand a Twitter query, check Twitter for, post a tweet, tweet this, post to X, reply on Twitter, full-archive search, what's trending, check mentions, my mentions, timeline, DMs, monitor account, research topic on Twitter, my bookmarks, archive bookmarks.

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。

Build AI agents using the Claude Agent SDK. Covers query functions, ClaudeSDKClient, custom tools, MCP servers, hooks, permissions, subagents, and browser control (Chrome extension, dev-browser skill). Use when creating agents, adding tools, configuring agent behavior, or automating browser 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 coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.

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。

Design human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.

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。

Coordinate SEO, LLM-readable site architecture, UX, content, monitoring, security, and external authority work as a task-plan-driven sequence. Use this skill when the user asks to plan, execute, validate, or route multi-skill website growth work; asks what to do next across semantic core, URL structure, internal linking, schema, llms.txt, UX journeys, analytics, citation monitoring, or white-hat link placement; or wants a central architect that preserves evidence, alarms, handoffs, and 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。

Generates a structured Project Brief — a comprehensive markdown document that extracts goals, architecture, knowledge file inventory, Custom Instructions summary, Memory contents, current state, ecosystem position, and key decisions from a Claude Project. Briefs serve as uploadable context documents: add one to any other Project for immediate deep awareness of the source Project's purpose, architecture, and progress. Use when user says "create a brief," "brief this project," "extract project context," "generate a project summary for another project," "I need to share this project's context," "prepare this project for cross-project reference," or "document this project." Also use when the user is preparing to work across Projects and needs portable context. Do NOT use for session handoffs, project audits, or Memory optimization (use rootnode-session-handoff, rootnode-project-audit, or rootnode-memory-optimization respectively, if available).

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。

Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

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。

Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.

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 the user is deciding, reviewing, or second-guessing a software architecture or tech-stack choice — monolith vs microservices vs serverless vs event-driven, backend language, database type, caching, CQRS/event sourcing, polyglot, or a migration like Strangler Fig. Triggers on "should I use microservices", "which database", "tech stack for", "system design", or "architecture review". For how to deploy, operate, and scale that design (CI/CD, cloud, Kubernetes, IaC), use devops.

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 a SaaS founder is deciding on or building a product-led growth motion — assessing PLG readiness, choosing freemium vs free trial vs reverse trial, defining activation and PQLs, designing self-serve onboarding and conversion triggers, building viral loops, or setting up PLG metrics (NRG, activation rate, TTV, NDR, CAC payback) and cohort analysis. Also for hybrid product-led-sales design and PLG-vs-sales-led GTM decisions. Not for paid acquisition channel mix (use channel-expert) or the mechanics of price points and tiers (use pricing).

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。

Observe and assess a garden using a structured sensory protocol adapted from Coordinate Remote Viewing. Covers pre-entry clearing (meditate checkpoint), Stage I gestalt impression, Stage II sensory layer (leaf, stem, root, soil), Stage III pattern recognition with AOL management, and garden health triage matrix (heal checkpoint). Use before any intervention, when plants show stress symptoms, at seasonal transitions, when evaluating a new garden site, during regular health monitoring, or after extreme weather events such as frost or heat waves.

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。

Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.

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。

Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

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。

Profile and optimize Shiny application performance using profvis, bindCache, memoise, async/promises, debounce/throttle, and ExtendedTask for long-running computations. Use when the app feels slow or unresponsive during user interaction, when server resources are exhausted under concurrent load, when specific operations create bottlenecks, or when preparing an app for production deployment with many concurrent users.

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。