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openai/transcribe

音声ファイルをテキストに書き起こし（文字起こし）し、必要に応じて話者識別（ダイアリゼーション）や既知の話者ヒントを追加します。ユーザーが音声や動画から音声を書き起こしたい、録音からテキストを抽出したい、インタビューやミーティングで話者をラベル付けしたい場合に使用します。

transcribe とは？

このスキルは、OpenAIのWhisperモデルを利用して音声ファイルを高精度に文字起こしするための機能を提供します。話者識別（ダイアリゼーション）機能をサポートしており、複数の話者がいるインタビューや会議の録音から、誰がいつ話したかを特定することが可能です。また、既知の話者の名前や声のサンプルを事前に登録する「既知話者ヒント」機能を備えており、識別精度を向上させることができます。開発者やAIエージェントユーザーは、このスキルを活用して、会議の議事録作成、インタビューの分析、ポッドキャストの文字起こしなど、様々な音声処理タスクを効率的に自動化できます。対応するファイル形式は一般的な音声・動画フォーマット（MP3, WAV, MP4, M4Aなど）で、API経由で簡単に利用できます。

対応~Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/openai/skills/tree/main/skills/transcribe
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このエージェントスキルを事前に読み込んだ状態で新しいチャットを開きます。

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ドキュメント

transcribe は何をしますか？

OpenAIのWhisperモデルを使用して音声ファイルをテキストに書き起こし、必要に応じて話者識別（ダイアリゼーション）や既知の話者ヒントを追加します。ユーザーが音声や動画から音声を書き起こしたい、録音からテキストを抽出したい、インタビューやミーティングで話者をラベル付けしたい場合に使用します。

使用方法

このスキルは、以下のようにして呼び出します。

openai/transcribe <音声ファイルのパス>

オプション

  • --diarize : 話者識別を有効にします。複数の話者がいる場合に、誰が話したかを特定します。
  • --speaker-hints <話者名1>,<話者名2>,... : 既知の話者の名前をカンマ区切りで指定します。識別精度が向上します。
  • --language <言語コード> : 音声の言語を指定します（例: ja, en）。自動検出もサポートしています。
  • --output-format <フォーマット> : 出力形式を指定します（txt, srt, vtt, json）。デフォルトはtxtです。
  • --model <モデル名> : 使用するWhisperモデルを指定します（tiny, base, small, medium, large）。デフォルトはmediumです。

使用例

基本的な文字起こし

openai/transcribe meeting_audio.mp3

話者識別を有効にして文字起こし

openai/transcribe --diarize interview.mp4

既知の話者を指定して文字起こし

openai/transcribe --diarize --speaker-hints "田中太郎","佐藤花子" podcast.wav

言語を指定して出力形式をSRTに指定

openai/transcribe --language ja --output-format srt lecture.m4a

サポートされているファイル形式

  • 音声: MP3, WAV, OGG, FLAC, M4A, AAC, WMA
  • 動画: MP4, AVI, MOV, MKV, WebM

注意事項

  • 大きなファイル（25MB以上）は自動的に分割されて処理されます。
  • 話者識別は音声の品質と話者の数によって精度が変わる場合があります。
  • 既知の話者ヒントは、実際の話者名と一致しない場合、識別精度に悪影響を与える可能性があります。
  • APIキーの使用量に応じて課金が発生する場合があります。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

openai/aspnet-core

Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.

openai/chatgpt-apps

Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

openai/cli-creator

Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read/write commands, return stable JSON, manage auth, and pair with a companion skill.

openai/cloudflare-deploy

Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.

openai/develop-web-game

Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with render_game_to_text.

openai/doc

Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.

openai/figma

Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.

openai/figma-code-connect-components

Connects Figma design components to code components using Code Connect mapping tools. Use when user says "code connect", "connect this component to code", "map this component", "link component to code", "create code connect mapping", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.

openai/figma-create-design-system-rules

Generates custom design system rules for the user's codebase. Use when user says "create design system rules", "generate rules for my project", "set up design rules", "customize design system guidelines", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.

openai/figma-create-new-file

Create a new blank Figma file. Use when the user wants to create a new Figma design or FigJam file, or when you need a new file before calling use_figma. Handles plan resolution via whoami if needed. Usage — /figma-create-new-file [editorType] [fileName] (e.g. /figma-create-new-file figjam My Whiteboard)

openai/figma-generate-design

Use this skill alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code'. This is the preferred workflow skill whenever the user wants to build or update a full page, screen, or view in Figma from code or a description. Discovers design system components, variables, and styles via search_design_system, imports them, and assembles screens incrementally section-by-section using design system tokens instead of hardcoded values.

openai/figma-generate-library

Build or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

openai/figma-implement-design

Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions "implement design", "generate code", "implement component", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.

openai/figma-use

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

openai/frontend-skill

Use when the task asks for a visually strong landing page, website, app, prototype, demo, or game UI. This skill enforces restrained composition, image-led hierarchy, cohesive content structure, and tasteful motion while avoiding generic cards, weak branding, and UI clutter.

openai/gh-address-comments

Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not logged in.

openai/gh-fix-ci

Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL.

openai/hatch-pet

Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.

openai/imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

openai/jupyter-notebook

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

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