「choose」の検索結果

Classify a bounded batch of stale PR CI failures and choose the safest response.

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。

Choose scoped cmux verification, add behavioral tests, and validate Swift test targets and wiring. Use when adding tests or deciding what local/CI evidence a change needs.

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 asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

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 asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.

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 when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

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 working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.

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 a detailed value proposition using a 6-part JTBD template — Who, Why, What before, How, What after, Alternatives. Use when creating a value proposition, analyzing customer value delivery, or articulating why customers should choose your product.

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。

Boot, reopen, update, or reset OpenWork PR previews. Discover script worlds; choose preview-desktop (app only), preview-den (Den only), preview-full (Den plus desktop), or preview-app-web locally, on a private Daytona URL, or on Freestyle for hands-on testing.

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 writing or modifying Jest unit tests (*.unit.js or *.unit.ts) or TypeScript type tests (*.types.ts) for Handsontable core, plugins, or utilities, or when a bug fix or internal refactor needs unit or type test coverage - covers Jest setup, test location conventions, mocking patterns, module aliases, type test patterns (no declare, real assignments, ESM + UMD patterns), and when to choose unit tests over E2E 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。

Find, evaluate, and download common purchasable CAD parts from step.parts, including named off-the-shelf actuators, servos, motors, electronics boards, connectors, screws, bolts, nuts, washers, bearings, standoffs, and other catalog components. Use when Codex needs to search the hosted step.parts catalog before creating simplified placeholder geometry, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part discovery.

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。

Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.

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。

Triage an incoming verdaccio/verdaccio GitHub issue against the codebase, the affected release line, and related issues, then choose the labels from the repository's existing taxonomy. Use whenever the user asks to triage, classify, assess, reproduce, prioritize, or label an issue, especially when an issue URL or number is supplied.

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。

Choose and apply the labels for a verdaccio/verdaccio pull request — exactly one release-line label plus content labels from the repository's existing taxonomy (typically one to three, more for a genuinely large multi-topic PR), never security, and AI assisted only on the author's own PR. Use right after gh pr create, when editing a PR, when reviewing a PR whose labels are missing or wrong, or when asked which labels a change should carry.

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。

Connect a new user's accounts, learn their real work, choose a voice, and set up concrete help.

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 an agent needs to build or operate the embedded KrillinAI CLI, choose a supported command, or interpret its JSON, manifest, subtitle, dubbing, render, cover, speech, and voice outputs.

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。

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

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。

Renders every state of a component you choose on a throwaway page, with mock data and a switcher, so you can work on each 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。

Renders a component you choose under every scenario that can reach it on a temporary page and stress tests it.

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 when the user asks to "open source this project", "prepare this repository for public release", "make this repo public", "check open-source readiness", "choose a license for this project", or "set up release automation" ahead of a public launch. Provides a release-readiness workflow covering secrets hygiene, licensing, documentation, CI, and language-specific packaging.

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。

Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species. Use when Codex needs to build, adapt, or review a general scRNA-seq QC pipeline; choose dataset-appropriate cell-level filters from QC distributions; run required scDblFinder-based doublet and ambient-RNA filtering; annotate cells with matched references or marker-based fallbacks; or generate global and per-group UMAP visualizations for large scRNA-seq datasets.

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 Creative Production is explicitly invoked or mentioned without a concrete request, when the user asks what Creative Production can do, or when the user wants help getting started without enough context to choose a workflow.

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。

Choose, lay out, critique, and explain data visualizations. Use when the user asks what visualization fits a dataset or goal, how a chart, dashboard, operational workspace, UML-like diagram, or software architecture diagram should be composed or interacted with, asks for visual page design, a layout mockup, generated large-screen and mobile concept images, or to be shown what a visualization could look like, when domain-native contextual surfaces or graphical backgrounds may help, when scrollytelling or parallax might be appropriate, wants a critique of an existing visualization, or needs guidance grounded in trusted visualization theory and practice. For advanced visual design or page-layout prompts where composition affects understanding, Codex must generate and show both large-screen and mobile portrait image concepts before implementation or text-only design handoff, plus mobile landscape when needed.

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。

Map a product's lifecycle stage and choose between extension, replacement, and retirement plays. Use when a product is maturing or declining and the next move isn't obvious.

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。

Choose a prioritization framework based on stage, team context, and stakeholder needs. Use when deciding between RICE, ICE, value/effort, or another scoring approach.

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。