「custom」の検索結果

Clone an existing video ad for a different product or offer. Analyzes the source video's style, pacing, camera work, dialogue, and tone, then adapts and generates a new Seedance 2.0 video customized for the user's product. End-to-end workflow: input video → analysis → adapted prompt → generation → delivery. Use when someone says "clone this ad", "make this ad but for my product", "recreate this video for my brand", or provides a video ad and a product image asking for a similar video.

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。

Select, customize, or extend the project's reusable motion-graphics card library and style templates. Use for choosing a visual style, adding a card, building a new style pack, or reusing a template in a HyperFrames video.

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。

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

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, deploy, operate, and troubleshoot Azure Local (formerly Azure Stack HCI): sizing and prerequisites, Arc registration, lifecycle updates, workloads (Azure Local VMs, AKS on Azure Local, images, disks, logical networks), SDN and network security, and failure triage — starting read-only and confirming before risky changes. WHEN: Azure Local, Azure Stack HCI, Arc resource bridge, custom location, Azure Local VM, Arc VM, AKS on Azure Local, AKS hybrid, SDN, Lifecycle Manager, Azure Local update, disconnected site.

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。

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

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, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, managed harness agent create, GitHub Copilot harness agent, create agent, update agent, add tool to agent, invoke agent, agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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。

End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into 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。

Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.

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。

Recover source-bound buyer jobs, struggling moments, desired progress, forces, workarounds, information acts, journey states, criteria, constraints, roles, locales, and authentic language. Use on approved ICP hypotheses plus customer, search, review, forum, procurement, support, or public-market evidence before designing an AI-visibility prompt architecture.

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 NPC AI in Unreal Engine 5 with Behavior Trees and Blackboards: composites (Selector/Sequence), tasks, decorators, services, and running the tree from an AIController. Use when creating enemy/NPC AI, BT_/BB_ assets, custom BTTask or BTService nodes, or when the user mentions Behavior Tree, Blackboard, AIController, BTTask, decorator, or 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 branching dialogue and narrative — a node/choice graph with conditions, variables, and localization hooks — and choose between authoring tools Ink and Yarn Spinner or a custom data-driven runner. Engine-neutral. Use when the user mentions dialogue system, branching dialogue, conversation tree, choices, Ink (.ink), Yarn Spinner (.yarn), or NPC dialogue.

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 event-driven, decoupled Godot 4.7 gameplay with signals and node groups: declare and emit custom signals, connect with Callables (incl. bind/one-shot), and broadcast to many nodes via groups and call_group. Use when wiring node communication in a Godot project, replacing tight references with signals, emitting/connecting events, or porting 3.x connect("sig", self, "method") 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。

Build and edit tile-based 2D levels in Godot 4.7 with TileMapLayer and TileSet: paint layers, set up collision/navigation/custom-data on tiles, autotile with terrain sets, and read/write cells from code (set_cell, get_cell_tile_data, local_to_map). Use when working with TileMapLayer nodes, .tres TileSets, autotiling, or migrating a deprecated TileMap node to TileMapLayer.

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。

Generate and render Mermaid diagrams for architecture docs, READMEs, PRs, terminals, chats, and CI as themed SVG, PNG, or ASCII/Unicode art. Use this skill whenever the user provides Mermaid code or .mmd files; asks for a flowchart, sequence/state/class diagram, ERD, XY chart, or architecture/workflow/data-model visualization; or wants to beautify, theme, batch-convert, or make a diagram terminal-friendly. Runs locally without a browser or DOM, with 15 built-in themes and custom colors.

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。

Repeatable HVE Core demo setup that simulates DT Coach sessions with a customer persona and scaffolds a hi-fi prototype - Brought to you by microsoft/hve-core

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, improve, refactor, replace, review, or validate prompts, instructions, agents, subagents, and skills. Use for Copilot customization cleanup, extending HVE workflows with project-specific capabilities, and parent-owned correction of material review findings.

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 and validate HVE Core agents, prompts, instructions, and skills with current frontmatter, package membership, delegation, tracking, documentation, and validation conventions. Use when authoring a GitHub Copilot customization artifact in this repository.

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 a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.

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。

Select, customize, or extend the project's reusable motion-graphics card library and style templates. Use for choosing a visual style, adding a card, building a new style pack, or reusing a template in a HyperFrames video.

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。

Installs, configures, customizes, or troubleshoots the Claude Code statusline (cwd, model, token counts). Use when the user wants to set up or change the statusline, switch minimal vs full layouts, show absolute token counts instead of a percentage, add cost via ccusage, or fix a statusline that is blank, silent, stuck, or stopped updating. Trigger phrases: 状态栏, 状态栏不显示, 状态栏空白, "statusline blank", "statusline broken".

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。

Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".

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 cold outreach to potential clients that gets replies — specific, useful, and about them — instead of the templated pitch that gets deleted. Use when asked to write a cold email to a prospect, get clients through outreach, cold pitch help, or reach out to potential customers. Produces a researched, personalized message that leads with their problem, a clear low-friction ask, proof you're credible without bragging, a subject line, and a short follow-up sequence — plus who to target and what to avoid so it lands as a helpful note, not spam.

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。

Produce a structured churn analysis that separates avoidable from unavoidable churn. Use when investigating why customers are leaving, identifying at-risk segments, calculating net revenue retention, or building a retention intervention plan. Produces a churn report with rate calculations, categorised reasons by avoidability, segment breakdown, timing analysis, early warning signals, and prioritised interventions ranked by estimated impact.

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。

Win a chargeback dispute — read the reason code, assemble the evidence packet, and write the rebuttal that actually persuades the bank. Use when asked to fight a chargeback, respond to a payment dispute, write a chargeback rebuttal, or contest a customer chargeback. Produces the reason-code decode, the required-evidence checklist for that code, the structured rebuttal letter, and an honest read on whether this one is winnable — so you fight the right disputes and concede the rest. For merchants.

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。