「service」の検索結果

Museで足りない連携や機能、繰り返す不具合を整理し、承認後にチームへ非公開で伝えます。送信済みの要望の確認や取り下げ、称賛の共有にも対応します。

• 未対応サービスとの連携を要望したいとき
• 繰り返す不具合を報告したいとき
• 過去の報告を確認・撤回したいとき

Find, compare, and share details on physical places near the user or in a specified area, including restaurants, cafes, bars, hotels, parks, attractions, shops, and businesses with local services. Not for itineraries, choosing a city or region, dated events or showtimes, or directions.

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。

Claude Codeの利用履歴から頻出する読み取り専用の操作を集計し、優先順位を付けた許可ルールをプロジェクト設定へ追加して、権限確認の表示を減らします。

• 頻出する読み取り操作の確認を減らしたいとき
• 利用実績から許可ルールを選びたいとき
• 既存設定を保ってプロジェクト設定を追加

Security-test a REST, GraphQL, or gRPC API with Strix — autonomous agents that enumerate endpoints from an OpenAPI/GraphQL schema (or by crawling), then actually exploit the API-specific vulnerability classes in the OWASP API Security Top 10 (2023) — broken object-level authorization (BOLA/IDOR), broken object property level authorization (excessive data exposure and mass assignment), broken function-level authorization, unrestricted resource consumption, SSRF, injection, and auth/token flaws. Every finding comes with a working proof-of-concept request. Use when the user asks to pentest, security-test, audit, or find vulnerabilities in an API, endpoint, or backend service.

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 voice AI agents with ElevenLabs. Use when creating voice assistants, customer service bots, interactive voice characters, or any real-time voice conversation experience.

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。

Fetch up-to-date documentation and code examples for any library, framework, SDK, CLI tool, or cloud service. Use whenever the user asks about a specific library — even well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot — because training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer. Do not rely on training data for API details, signatures, or configuration options — they are frequently out of date. Prefer this over web search for library documentation.

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。

Fetches current, version-specific library documentation and code examples through the Context7 MCP server. Use whenever the user asks about a library, framework, SDK, API, CLI tool, or cloud service, including API syntax, configuration, setup instructions, version migration, CLI usage, and library-specific debugging. Use when generating code that calls a third-party library, and when the user names a version such as Next.js 16 or React 19. Use even for well-known libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Django, and Spring Boot, because training data may not reflect recent changes. Prefer this over web search for library documentation. Do not use it for refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts, or when the user has already supplied the relevant documentation.

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。

Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer — do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.

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。

Contributing to the Twenty codebase itself (twentyhq/twenty server internals), not for building apps on top of Twenty. Create cache services and transformation utilities for syncable entities in Twenty. Use when implementing entity-to-flat conversions, input DTO transpilation to universal flat entities, or cache recomputation for syncable entities.

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Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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。

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers.

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。

Automated Cherry Studio review for local branches, PRs, commits, files, architecture docs, and repository skills. Use for code or documentation reviews that need project-specific naming, main/renderer/shared placement and dependency rules, IpcApi and DataApi boundaries, lifecycle/service ownership, renderer hooks, React/UI conventions, and tests. Review depth adapts to diff size and runtime subagent capability (single-agent or multi-agent reviewer-verifier). Report-only by default; code fixes and GitHub submission each require explicit invocation-time authorization (`fix` / `submit`). Normal-review prompts and safe interruption behavior follow the interaction contract below. To diagnose gaps in the skill after a review session, run `/gh-pr-review diag`.

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。

Resolves free-text scientific labels to ontology term IDs and validates existing CURIEs against the EBI Ontology Lookup Service (OLS4). Also looks up prefixes in Bioregistry, resolves compact identifiers via Identifiers.org, maps lab shorthand with ZOOMA, and builds Ontobee term pages. Use whenever an ontology identifier must be produced or checked - annotating tissue, cell type, disease, phenotype, assay, chemical, organism, sex, or developmental stage fields; preparing metadata for GEO, ENA, BioSamples, CELLxGENE, HCA, or ISA-Tab submission; auditing a metadata table of term IDs; checking whether a term is obsolete and what replaced it; or deciding HPO vs HP. Triggers include "ontology term", "ontology ID", "CURIE", "controlled vocabulary", "UBERON", "CL:", "MONDO", "HPO", "EFO", "ChEBI", "NCBITaxon", "GO term", "PATO", "Zooma", "Bioregistry", "Identifiers.org", "Ontobee", "annotate this tissue/cell type/disease", and any request to emit or verify an identifier shaped like PREFIX:0001234.

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。

Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

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。

Operates Fictiv (app.fictiv.com), the on-demand manufacturing platform, end to end in the user's browser. Covers uploading CAD parts, configuring process, material, finish, threads, tolerances and inspections, getting instant or manual quotes, reading and fixing DFM feedback, choosing lead time and region, checking out and paying (card or PO), tracking orders, reordering, and troubleshooting. Applies when the user mentions Fictiv, wants a part CNC machined, 3D printed, sheet-metal fabricated, urethane cast, injection or compression molded, or die cast through an online service, asks to "get a quote" or "order parts" for a STEP/SLDPRT/STL file, wants to check a Fictiv quote or order status, or has a problem with a Fictiv upload, DFM warning, price or checkout. Also applies when the user wants custom parts manufactured and has a Fictiv account, even if Fictiv is not named.

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。

Install and use the official AI Headshot Generator package, pinned by digest, for paid hosted work on the Beatra service.

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 production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.

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。

Deploy and manage Azure Kubernetes Service clusters. Configure node pools, networking, and integrations. Use when running Kubernetes workloads on Azure.

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。

Send SMS messages with Azure Communication Services SMS Java SDK. Use when implementing SMS notifications, alerts, OTP delivery, bulk messaging, or delivery reports.

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。

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.

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。

AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations.

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。

Deploy containers on ECS and Fargate. Configure task definitions, services, and load balancing. Use when running containerized workloads on AWS.

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。

AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows.

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。

Review repository architecture using Ontoly Software Graph and MCP capabilities. Use when asked to explain architecture, module boundaries, package topology, service ownership, or architectural risk.

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。