Transcribe local MP4/M4A/MP3/WAV/WEBM audio or video with ffmpeg and Whisper, then optionally produce diarized transcripts, customer-facing meeting minutes, action items, and PPT-ready summaries. Use when: 文字起こし, transcription, transcribe mp4, meeting transcript, mp4から文字起こし, 録画から議事録, 顧客向け議事録, 話者分離, diarization, whisper CLI, ffmpeg.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
ローカルにある音声/動画ファイルを文字起こしし、必要に応じて議事録・アクションアイテム・PPT要点まで整理する skill。
.txt.txt / .md.md.md.md.md入力確認
ツール確認
ffmpeg, ffprobe, whisper CLI が使えるか確認するメディア確認
ffprobe で duration を取得する文字起こし実行
$env:PYTHONIOENCODING='utf-8'
whisper "<media-file>" --language Japanese --task transcribe --model turbo --fp16 False --verbose False --output_format txt --output_dir "<output-dir>"
品質確認
.txt の先頭数十行を読んで破綻していないか確認する必要に応じて話者分離
必要に応じて整形
ffmpeg / whisper がない場合は中断.txt を出力して終了話者A / 話者B などのラベルで一度出力する.txt が生成されているまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design, review, and debug agent workflows, and decide when a request should use a prompt, instruction, skill, agent, or hook before escalating to multi-agent design. Use for .agent.md / .instructions.md / .prompt.md / AGENTS.md work, workflow architecture, orchestration planning, scheduled automation model allocation, or when agent workflows may be overkill. Triggers on 'agent workflow', 'create agent', 'automation models', 'ワークフロー設計', 'orchestrator'.
Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.
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 historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session cleanup.
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 reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.
Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid. Also use for Archify, interactive or animated HTML diagrams, インタラクティブ図, 動く構成図. When an editable .drawio file is required, use the draw.io workflow instead and treat Archify HTML only as a supplement.
Generate ASCII-art text banners for terminals, READMEs, and CLI startup messages. Use when a user asks for an ASCII banner, text art, バナー, アスキーアート, or a decorative header for a script/README.