「wants」の検索結果

Plans, researches and writes comparison pages: "[rival] alternatives", "[product] vs [rival]", "[rival A] vs [rival B]" and switching guides. Builds each page from a ledger of sourced, dated facts and ends with an honest verdict on who should choose which product. Use when someone asks for an "alternative page", a "vs page", a "competitor comparison", "comparison landing pages", "why us instead of them", or wants existing comparison pages checked for outdated prices and claims that cannot be backed up.

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 a founder from real decisions to a short set of written company values that people can act on: collects incidents, tests each candidate value, writes it as a trade-off with behaviors and a story, and wires it into hiring, feedback and promotion. Use when someone asks to "define our company values", "write a values document", "our values are generic, fix them", "turn our values into interview questions", "what should our culture be before we hire", or wants to check whether the team lives the values it has. Produces VALUES.md, an evidence log and an interview kit.

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。

Create 2D art and animation in Blender using Grease Pencil and Python. Use when the user wants to draw strokes programmatically, create 2D animations, build Grease Pencil objects from code, manage GP layers and frames, apply GP modifiers, set up drawing guides, or script any Grease Pencil workflow in Blender.

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。

Browser Use is an open-source Python library that lets an LLM agent drive a real browser to navigate sites, fill forms, click and extract data from a plain-English task. Use when the user wants an AI agent to automate a website, scrape pages that need clicking or login, return structured data from the web, add browser tools to a coding agent, or run browser agents locally or in the cloud.

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 custom Blender add-ons with Python. Use when the user wants to create a Blender add-on, register operators, build UI panels, add custom properties, create menus, package an add-on for distribution, or extend Blender with custom tools and 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。

Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

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。

Animates 3D objects and characters in Blender with Python (bpy): keyframes, armatures and IK rigs, shape keys, F-Curves, the NLA editor and drivers. Use when the user wants to keyframe properties, create armatures and rigs, set up IK/FK chains, animate shape keys for facial animation, edit F-Curves, blend actions with NLA strips, add drivers, or script any animation workflow in Blender from the terminal.

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。

Write and run Blender Python scripts for 3D automation and procedural modeling. Use when the user wants to automate Blender tasks, create 3D models from code, run headless scripts, manipulate scenes, batch process .blend files, build geometry with bmesh, apply modifiers, generate procedural shapes, or import/export 3D models using the bpy API.

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。

Automate Blender rendering from the command line. Use when the user wants to set up renders, batch render scenes, configure Cycles or EEVEE, set up cameras and lights, render animations, create materials and shaders, or build a render pipeline with Blender Python scripting.

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。

Automate motion capture and tracking workflows in Blender with Python. Use when the user wants to import BVH or FBX mocap data, retarget motion to armatures, track camera or object motion from video, solve camera motion, clean up motion capture data, or script any tracking pipeline in Blender.

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。

Cerebras Inference is a hosted LLM API that runs open models on Cerebras wafer-scale chips at thousands of tokens per second, through an OpenAI-compatible endpoint. Use when a user wants the lowest-latency chat, code completion or agent loop, asks how to call the Cerebras API or SDK, needs streaming, tool calling, strict JSON schema output or reasoning control, or hits a "model not found" error from a retired Cerebras model ID.

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。

Centrifugo is a self-hosted real-time messaging server: your backend publishes over an HTTP or gRPC API and browsers or mobile apps receive messages over WebSocket, SSE or HTTP streaming. Use when the user wants chat, live notifications, presence ("who is online"), message history with recovery, or any WebSocket pub/sub without writing a socket 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。

Blender's compositor post-processes renders with a node graph, and this skill scripts it from Python (bpy) in Blender 5.x. Use when the user wants to set up compositor nodes, add post-processing effects, color correct renders, combine render passes, apply blur or glare, key green screens, write passes to multilayer EXR, or port a compositing script that broke after Blender 5.0 (scene.node_tree, CompositorNodeComposite, glare_type and file_slots are gone).

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。

Arize is an AI observability platform, and Phoenix is its open-source tracing and evaluation tool for LLM applications. Use this skill when the user wants to trace OpenAI or LangChain calls, run a local Phoenix server, score RAG answers for faithfulness, debug slow or wrong LLM responses, or send OpenTelemetry traces to Arize for production monitoring.

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。

Appium is an open-source framework that automates native, hybrid and mobile-web apps on iOS and Android through the WebDriver protocol. Use when the user wants to set up Appium 3, write mobile UI tests in JavaScript (WebdriverIO) or Python, pick capabilities, run gestures, or fix "appium", "XCUITest", "UiAutomator2", "mobile automation" problems. For React Native-only testing, see detox.

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 to set up authentication and authorization for a web application. Use when the user mentions "auth," "login," "OAuth," "SSO," "single sign-on," "role-based access," "RBAC," "permissions," "user roles," "access control," "authentication," or "authorization." Covers OAuth 2.0 provider integration, session management, and role/permission architecture. For JWT-specific tasks, see jwt-handler. For security review, see security-audit.

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 to perform load testing or performance testing using Artillery with YAML-based scenario definitions. Also use when the user mentions "artillery," "YAML load test," "WebSocket testing," "Socket.io load test," "scenario-based load testing," or "artillery run." For JavaScript-based load testing, see k6.

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。

Builds Agent2Agent (A2A) servers and clients, the open protocol (originally from Google, now under the Linux Foundation) that lets AI agents from different frameworks call each other. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.

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。

Converts repeated AI coding-agent workflows into reusable skill packages with triggers, guardrails, examples, and verification checks. Use when a user wants to turn a prompt, checklist, AGENTS.md section, Claude/Codex workflow, or recurring agent task into a portable SKILL.md.

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。

Plans a controlled experiment (A/B test) so its result can be trusted: writes the hypothesis, picks one primary metric and the guardrails, computes sample size and run time, specifies how visitors are assigned and when exposure is logged, and reads out the result with a confidence interval. Use when someone says "set up an A/B test", "split test this page", "how many visitors do I need", "how long should the experiment run", "is this result significant", "can I stop the test early", or wants to test a headline, price, layout or onboarding change against the current version.

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。

AG-UI (Agent-User Interaction Protocol) is an open, event-based protocol that streams an AI agent's text, tool calls, state and lifecycle events to a frontend over HTTP and Server-Sent Events. Use when the user wants to build an AG-UI server, connect an agent or framework (LangGraph, Mastra, CrewAI, Pydantic AI) to a React or custom UI, stream agent progress and state, or use CopilotKit with AG-UI.

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 indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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 wants a Xiaohongshu posting calendar, weekly review board, first-10-post launch plan, 10-20-post positioning test, or data-based next-step plan. Trigger phrases: "排期", "复盘", "30 天计划", "前10条", "10-20条测试", "发了没数据", "weekly review", "content calendar", "XHS schedule". Do not use for first-draft editing or initial account positioning.

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 wants WorkBuddy or another agent to remember an account's positioning, voice, boundaries, data, trust assets, personal IP story, and content rules across sessions. Trigger phrases: "账号档案", "记住我的账号", "写入记忆", "人设垂直", "信任名片", "brand profile", "account memory", "style guide". Do not use for one-off post drafting unless profile data is missing.

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。