241 件 ・ 関連度順
概要と使いどころ
Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.
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, install, pair, and operate the Life Recorder iPhone-to-Mac local transcription system. Use when a user asks to reproduce or troubleshoot this project.
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。
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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 user asks YouTube video extraction, get, fetch, transcripts, subtitles, or captions. Writes video details and transcription into structured markdown file.
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。
Transcribe audio and video from URLs (YouTube, direct media links) using WhisperKit locally. Optionally analyze transcripts with AI when explicitly requested. Use when users provide URLs to media content and request transcription or speech-to-text conversion.
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。
Get content from Bilibili videos: official subtitles, danmaku (density/peaks/sample), comments. When no subtitles exist (no 投稿字幕), fall back to downloading audio + ASR transcription via FunASR.
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 code-driven explanatory animations and precise vector graphics with Manim, optionally using SandBase for generated images, narration, music, or transcription. Use for mathematical, scientific, algorithmic, or diagrammatic animation; use a video-generation workflow for photoreal footage.
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 production OpenAI Realtime voice agents and low-latency spoken interactions with live audio sessions, WebRTC or WebSocket transport, voice activity detection, tool/function calling, prompt design, logging, consent, privacy, safety, latency, and cost controls. Use for speech-to-speech agents and live voice UX; do not use for separate request-based transcription, text-to-speech, or offline audio generation work.
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。
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 Amazon Transcribe for AWS-based speech-to-text production: batch S3 transcription, real-time streaming, captions/subtitles, diarization, channel identification, custom vocabularies, vocabulary filters, language identification, PII/PHI handling, toxicity detection, Call Analytics, Medical, and secure S3/IAM/KMS 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。
Use this skill when a media-production agent needs Google Cloud speech and voice services for transcription, captions/subtitles, long-form audio analysis, live caption planning, Text-to-Speech narration, Chirp 3 / Gemini-TTS voice selection, consented custom voice workflows, dubbing/localization planning, pricing/quotas/region checks, or speech-related safety and data-governance 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。
Use for Deepgram speech and voice production workflows: speech-to-text transcription, live captions, diarization, audio intelligence, Aura text-to-speech, Flux and Voice Agent live audio, model selection, cost/limits/privacy checks, artifact custody, and production QA.
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。
Operate AudioShake's cloud source-separation service (developer.audioshake.ai) to split a recording into stems. Use when a task involves isolating vocals, drums, bass, guitar, piano, keys, strings, or winds from music; separating dialogue / music / effects (DME) for film-TV post-production, localization, and dubbing; splitting a mixed recording into one stem per speaker; lyric transcription or word-level alignment; music detection/identification; or speech denoise/dereverb. Covers the Tasks API job lifecycle (assets, targets, formats, polling vs webhooks, credits, limits), choosing which stem targets a production actually needs, reviewing separated stems for bleed / artifacts / transient smearing / phase problems and repairing them, the rights and consent questions raised by separating copyrighted or third-party recordings, and the decision boundary for when a local open model such as Demucs is the better route than the API. Not for generating or synthesizing new audio, mixing, or mastering.
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, direct, integrate, and quality-control ElevenLabs text-to-speech for narration, character performance, multilingual media, long-form voiceover, and streamed or batch AI-content audio. Use when selecting ElevenLabs models or voices; preparing spoken text; controlling pronunciation, pacing, emotion, and code-switching; using TTS APIs; cloning or designing a voice with consent; repairing artifacts; mastering deliverables; or evaluating generated speech. Excludes music, sound-effect generation, conversational-agent design, and general speech recognition except transcription used to verify TTS.
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。
Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before footprinting, choosing between cleavage-based and aggregate-based footprinters, or comparing differential TF activity between conditions.
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。
Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
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。
Agent Media OS, the single skill for every media need in a HyperFrames project. Resolve BGM, SFX, image, icon, brand logo, voice, color grade, or LUT into a frozen local file or paste-ready block + ledger record (one verb, `resolve`); generate via TTS / music / image models when the catalog misses; produce voiceover, transcription, captions, and background removal through one shared audio engine; operate on media (cut / reframe / transform); and reuse assets across projects. Keeps search noise on disk, hands the agent one path or block. Use for any audio, image, icon, logo, voiceover, caption, color-grading, or media-asset need.
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 SDK for Python (the `ai` package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.
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 rove 仓库内跑 auto-motion——把 transcription.srt 拆成多段 MG 动画镜头并拼接成竖屏视频(storyboard 分镜 + theme.md 全片主题 + 逐镜头 claude -p 子进程 + ffmpeg 拼接)。本 skill 是薄 wrapper:解析 auto-motion 模板根,继承 rove 品牌 theme,执行逻辑以 auto-motion 仓库的 canonical SKILL.md 为准。当用户说"跑 auto-motion"、"把这个字幕稿/口播稿做成视频"、"给 rove 做一条 MG 宣传片"时使用。
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。
Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Check for Faster-Whisper (preferred - 4-5x faster) workflow skill. Use this skill when the user needs Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Preprocesses photographed sheets of many business cards — slicing each into overlapping high-resolution tiles and de-glaring them with container tooling (OpenCV/ImageMagick) — then reads every card via cheap parallel temperature-0 API calls (Haiku or Sonnet) using a distilled extraction prompt, and writes deduped contact fields to a CSV. Use when a user has photos or scans holding multiple business cards per image, mentions glare or unreadable cards, batch card transcription, contact extraction, or wants to read many cards without an expensive in-conversation pass. Triggers on 'business cards', 'card scan', 'extract contacts', 'read these cards', 'card glare', 'too many cards per photo'.
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。