「prompt」の検索結果

Schedule a prompt to run later, once or on a repeating cron expression.

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。

在 W8A8 动态量化后，用 MindIE-SD 冒烟测试 DiT 量化权重是否可加载（可选 skill）。与 `quant-tuning-evaluate`（DiT 扩展节）的区别：本 skill 仅做"能跑通 + 权重可用"的轻量验证（4~16 条 prompt，几分钟出结果），不算分；后者产出视频集交 `quant-tuning-score-dit`（AISBench-VBench 16 维度）评分，是调优回路一环。不引入 vLLM-Ascend、不做精度指标评估（FID/CLIP-score 等）。

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 to train a reusable face-faithful Soul Character identity on Higgsfield. One-time training returns a reference_id passed to Soul models in higgsfield-generate via `--soul-id`. Use this skill whenever: "train soul character", "create my Soul", "train my face", "make my digital twin", "build me an avatar", "create a character of me", "set up identity for video", "I want my face in generated images", "face-faithful character", "reusable character", "brand character training", "build a character from photos", "soul training", "soul-id create". After training, logs the soul_reference_id to `brand/assets.md`. NOT for: one-shot face swaps (use higgsfield-generate with --image), fictional/non-photo avatars (use higgsfield-generate with a prompt). Requires Higgsfield CLI + Basic plan or higher.

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 images using the brand's visual identity and Gemini API. Reads brand/creative-kit.md for visual style, crafts narrative prompts, and produces images via Nano Banana Pro (gemini-3-pro-image-preview). Supports on-brand and freestyle modes. Use when the user needs a blog header, social graphic, product shot, hero image, banner, thumbnail, or any generated image. Also use proactively when building content that would benefit from visuals. Triggers on "generate image", "create image", "make me an image", "blog header", "social graphic", "product shot", "hero image", "banner", "thumbnail", "I need an image", "visual for", or any request for generated artwork. Even if they just say "image" or "picture for this" — this is the skill.

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。

Rules for building offline-first Progressive Web Apps that launch instantly from the home screen. Use when building, reviewing or debugging a PWA, or anything touching a service worker, a web app manifest, precaching, an in-app update prompt, iOS home screen behaviour, or safe-area/notch layout.

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 marketplaces for AI models and prompts. TRIGGERS - Use when user needs help with ai-marketplace-builder related 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。

Build AI prompt chains and multi-step workflows for complex tasks. TRIGGERS - Use when user needs help with ai-chain-builder related 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。

Design AI A/B testing systems for prompts and models. TRIGGERS - Use when user needs help with ai-ab-test-system related 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。

Design and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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, change or cancel a prompt that runs on a schedule, in whatever language the user asks. Use when the user says things like "every weekday at 8:30", "în fiecare zi lucrătoare la 8:30", "each morning", "remind me at noon", or wants to reschedule, edit or stop a scheduled 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。

Use this when setting up a GitHub repo to push its Nix builds to Juspay's shared OSS Attic cache (cache.nixos.asia/oss) — adds the substituter to flake.nix, a nix-cache.yml GitHub Actions workflow, and prompts for the ATTIC_TOKEN secret.

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。

把 Claude 腔的中文改写成平实自然的中文。六段流水线的第四段： 专拆 Claude 的助手腔 —— 对话残留、客套、解释癖。稿子整体的 AI 模板句式归第二段管。 规则提炼自开源项目 gvzdv/claudish-to-english（2.4k star）的内置改写 prompt， 本地化成中文，由 agent 直接执行，不需要装它的插件。

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 streaming durable workflow updates to a UI in real time — live order status pages that animate as steps complete, AI agent token streaming from a function to the browser, log tailing for long-running jobs, or human-in-the-loop approval flows that publish a prompt and wait for a user reply. Covers Inngest v4 native realtime: defining typed channels, publishing from inside step.run, minting subscription tokens via server actions, and consuming the stream from React/Next.js client components.

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 building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.

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。

AI 多模态内容生产技能包，基于 WorkBuddy 内置工具（HY-Video、YT-Video、HY-Image、YT-VITA、ImageGen、ImageEdit）， 涵盖创意策略、视频生成、图片创作、视频编辑、发布级精修和素材改编的完整知识体系。 触发词：视频生成、图片创作、AI创作、智能剪辑、视频翻译、素材分析、内容合成、Prompt工程、创意策划、情绪板、广告方向、精修。

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。

视频生成专家（维欧 · Veo）。擅长文生视频（HY-Video-1.5）、图生视频（YT-Video-2.0）、人物视频（YT-Video-HumanActor）、特效视频（YT-Video-FX）、视频 Prompt 工程和多模型选型。 触发词："帮我生成一段产品宣传视频"、"把这张图变成动态视频"、"用这张照片生成人物说话的视频"、"给这张图加飘雪特效"。

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。

Control and interact with a live browser session on any scraped page — click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Use when the user needs to interact with a webpage beyond simple scraping: logging into a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on "interact", "click", "fill out the form", "log in to", "sign in", "submit", "paginated", "next page", "infinite scroll", "interact with the page", "navigate to", "open a session", or "scrape failed".

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。

Teaches you how to delegate tasks to Cowork so clearly that you get exactly what you asked for — every time. Use when learning how to give better instructions, for improving your delegation technique, during early Cowork adoption when results feel off, when writing clearer task descriptions, or when the user says "how do I ask cowork to do things", "delegation tips", "better instructions", "cowork isn't doing what I want", "help me delegate", "my results are bad", "how do I give better prompts", "cowork keeps getting it wrong", "I don't know how to use this", or "how do I get good results".

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。

Run AI agent tasks on a Volcengine (火山引擎) cloud phone via the Mobile Use Agent OpenAPI. Use when the user asks to drive, automate, operate, or test an Android app on a cloud phone for enterprise use cases (e.g. reply to WeCom customer messages, add leads in a CRM, clock in on DingTalk, check invoicing stats, query inventory in a supply-chain app, ship e-commerce orders, approve OA requests) or personal ones (open 小红书 and search, place an order, check nearby places on a map), or to check status, fetch result, cancel, or list a Mobile Use run. Requires one-time setup to store Volcengine AK/SK locally; ProductId and PodId can be saved as a default device during setup, the user prompt is provided per run. Supports optional GPS injection (GpsInfo) with user consent (auto-asked only for location-related 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。

Use this skill whenever the user wants to use AI agents to work with Penpot design files via the Penpot MCP Server. Triggers include: using Penpot through an AI agent, design files, design systems, design tokens, Penpot MCP, design-to-code, generating UI from design, auditing a design system, creating components/variants, renaming layers, exporting assets from Penpot, adding flows, interactions, animations, overlays, or prototyping in Penpot, or prompting an AI agent to read/modify a Penpot file. Also triggers when the user wants to set up Penpot MCP, connect any MCP-compatible AI agent or IDE to Penpot, or produce production-ready HTML/CSS/React from a Penpot design. Use this skill for Penpot-agent workflows — design, code, audit, prototyping, or setup.

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 how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.

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 validating, benchmarking, or monitoring LLM application performance. Keywords: RAG evaluation, LLM-as-a-judge, CI/CD gating, trajectory scoring, test suites, prompt quality.

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 and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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 optimizing token usage, KV cache efficiency, or context window management for LLM agents. Keywords: context optimization, KV cache, prompt caching, token budget, semantic pruning, lost-in-the-middle.

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。