「output」の検索結果

Read, edit, redline, comment on, and create Microsoft Word .docx files. Use to fill out or edit a Word doc, redline a contract with tracked changes, add/resolve comments, replace text keeping its formatting, restyle headings/fonts, edit tables, or read/extract a .docx as Markdown or text. Also BUILD a new .docx — from Markdown or programmatically (code that outputs a Word report with headings, tables, images). Not for PDF, Google Docs, Excel, PowerPoint, or .doc.

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。

Judgment rules for user-facing text and docs: CLI and diagnostic output, error and help text, README and docs structure, code comments, titles, and generated reports, decks, or exports. Use when writing or changing any user-visible string, when adding or restructuring docs or deciding which page owns a fact, when a page is about to record a version, a deployment state, or a value the code already owns, when a comment, title, or artifact could carry the reasoning or an abandoned option behind the change, when a behavior change needs its copy sites swept, or when reviewing a diff that touches copy or docs.

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。

One-time setup for absolute: interview how you want it to behave (output style, autonomy, TDD strictness, spec dir, families) + detect the stack once, then write `.absolute.config.json` (project, committed) and `~/.absolute/config.json` (user defaults + per-project overrides). Every other absolute-* command reads it instead of re-detecting; non-blocking — commands proceed without it and soft-recommend it. Triggers on "absolute init", "set up absolute", "initialize absolute", "configure absolute", "first-time setup", "remember my conventions for this repo".

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。

Base template for Stage2 rebuttal refinement. Use when creating or extending conference-specific Stage2 skills so all venues share the same high-quality writing constraints, JSON output format, and normalization rules.

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。

Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.

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。

Summarize text using extractive, abstractive, hierarchical, and multi-document techniques, producing concise outputs at configurable detail levels. Use when the user requests summarization or provides relevant inputs for this 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。

Create polished, audience-tailored presentations from outlines or documents, with support for Marp, reveal.js, Google Slides, and PPTX output formats. Use when the user requests presentation creation or provides relevant inputs for this 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。

Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this 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。

Run a skill's evals and report results. Use when the user wants to evaluate a skill, run evals, benchmark a skill, validate triggers, optimize a description, or grade skill 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。

Connect, verify, and troubleshoot Xquik's remote MCP server. Use when a user needs MCP setup, OAuth recovery, tool discovery, endpoint execution, or a connection check in ChatGPT, Claude, Codex, Cursor, VS Code, or another compatible client. Covers the endpoint, OAuth and the API key fallback, the docs, search, and execute tools with their inputs and outputs, errors, and the confirmation rules for private, metered, persistent, or state-changing calls. Not affiliated with X Corp.

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。

当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 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。

当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用 output-formatting 而非本 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。

Think and work like an expert Edge / Embedded AI Engineer. Use when a task calls for Edge / Embedded AI Engineer judgment. Reasons from tensor-arena budgets, full-int8 PTQ with representative calibration, and TFLM/CMSIS-NN or Vela/Ethos-U compile paths through ONNX Runtime QNN HTP and mobile delegates—treating train–serve preprocessing skew, float thresholds on quantized outputs, and NPU operator fallback as first-class failure modes.

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。

ifly-pdf-image-ocr skill supporting both image OCR (AI-powered LLM OCR) and PDF document recognition. Use when user asks to OCR images, extract text from images/PDFs, convert PDF to Word/Markdown, or perform any OCR tasks on images or PDFs. Supports multi-language text extraction, document layout understanding, and various output formats.

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 Health — runs 19 deterministic quality checks on HTML output. Use when the user says /reimagine-it audit, "audit this page", "check design quality", or wants to verify craft-floor compliance before shipping. Catches blank plates, missing focus rings, non-compositor motion, off-palette accents, dead motion, fabricated content. No LLM, no API key. CI-ready with --json and exit codes. Part of the reimagine-it Content-Derived Design suite.

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 formatted .xlsx financial statement workbooks from XBRL statement data. Uses Bash + openpyxl (Python) following Anthropic FSI xlsx-author conventions for professional Excel output with calculation arc cross-validation.

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 enhancing, upscaling, restoring, denoising, sharpening, deinterlacing, stabilizing, motion-deblurring, colorizing, SDR-to-HDR converting, or frame-rate/slow-motion converting video (and secondarily images) with Topaz Labs — whether cleaning up AI-generated video, compressed UGC, or old archival footage, or building a generate-low-res-then-upscale delivery pipeline. Covers choosing the right named Topaz model for a specific footage problem, setting parameters, driving the Topaz Platform (Video/Image) REST API's async job lifecycle, judging when enhancement helps versus when it introduces artifacts (over-smoothing, temporal shimmer, face hallucination), and QA of enhanced output. Do not use for originating new video content, non-Topaz upscalers, or purely editorial cuts.

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 and understand audio with OpenAI request-based audio APIs and audio-capable chat models, including text-to-speech, transcription, translation, multimodal audio input/output, model routing, prompt and performance direction, artifact custody, approval gates, and safety/rights review. Use for non-realtime OpenAI audio production; route continuous live voice agents to the separate realtime voice 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。

Use when an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.

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 speech and clone voices with Fish Audio — its hosted TTS API (S2.1-Pro / S2-Pro / S1 model lineup, REST + WebSocket streaming, instant and persistent voice cloning) and its open-weight OpenAudio S1-mini / Fish-Speech models for self-hosting. Use this skill when an agent must generate narration or dialogue through Fish Audio, choose between Fish Audio's hosted models and open weights, clone a voice from reference audio, author emotion/tone/special markers for expressive delivery, estimate cost from UTF-8 bytes, wire real-time streaming for a voice agent, decide whether self-hosting beats the API, or review Fish Audio TTS output for production. Do not use it to pick a different provider — it covers Fish Audio specifically.

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 and operate Amazon Nova Reel video-generation jobs through Amazon Bedrock. Use for Nova Reel text-to-video, image-conditioned animation, automated or manual multi-shot storyboards, async S3 delivery, cost and approval gates, production continuity, output custody, and AWS-specific safety, privacy, IAM, and provenance decisions.

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。

Production guidance for international BytePlus Seed Speech text-to-speech. Use for selecting TTS 1.0 versus 2.0, bidirectional or unidirectional streaming, current voices and languages, prompt/prosody controls, subtitle timing validation, billing and concurrency, authorized replicated voices, privacy, error handling, and output QA. Do not use for mainland-China Volcengine Doubao Speech endpoints.

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 NVIDIA Speech NIM microservices for speech production and voice workflows, including self-hosted ASR/STT, TTS, text translation, speech-to-speech pipeline design, Riva Python client integration, deployment planning, GPU/runtime sizing, licensing/privacy review, and production QA for transcription, captioning, voice agents, localization, and synthetic voice output.

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 evaluate low-latency spoken, multimodal, and translation experiences with Google Gemini Live API and Gemini Enterprise Agent Platform Live API. Use when a media-production or voice-agent workflow needs real-time audio input/output, barge-in, voice configuration, live transcription, live translation, tool/function calling during a spoken session, WebSocket session design, latency QA, quotas/cost review, or Google/Vertex data-governance tradeoffs.

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。