How do I install the Whisper Transcribe Setup skill?

Run npx skillmds@latest add connect0459/whisper-transcribe-setup in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Whisper Transcribe Setup safe to use?

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Which AI agents work with Whisper Transcribe Setup?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Whisper Transcribe Setup free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Whisper Transcribe Setup?

connect0459 (@connect0459) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Whisper Transcribe Setup by connect0459

Name: Whisper Transcribe Setup
Author: connect0459

Whisper Transcribe Setup

Build the whisper-transcribe Rust binary from source and download the default Whisper model (~809 MB, ggml-large-v3-turbo). Installs the binary to ~/.local/bin/whisper-transcribe and the model to ~/.local/share/whisper-transcribe/models/. Runs English and Japanese smoke tests to verify the install works end-to-end. Trigger whenever the user wants to set up whisper transcription, install the whisper backend, build the Rust binary, download the Whisper model, or prepare for local or YouTube transcription — including phrases like "set up whisper", "install whisper", "build whisper-transcribe", "I need to transcribe audio", "prepare for transcription", or any time the user is missing the binary or model required by youtube-transcribe-local or youtube-transcribe-transcribe-youtube. Idempotent: safe to re-run. Build skips when the binary is current; model download skips when the file already exists; smoke tests always run.

connect0459 7f1862d 2 files · 10.8 KB Updated Sep 17, 2026

File contents

whisper-transcribe-setup

Build the whisper-transcribe Rust binary from src/whisper-transcribe/ in this repository and download the default Whisper model. The binary and model are the shared backend for /connect0459-agent-skills:youtube-transcribe-local and /connect0459-agent-skills:youtube-transcribe-transcribe-youtube.

Script

<skill-path>/scripts/setup [--model NAME] [--no-smoke-test]

Defaults:

Binary install path: ~/.local/bin/whisper-transcribe
Model directory: ~/.local/share/whisper-transcribe/models/
Model: large-v3-turbo (~809 MB quantised, multilingual, fast on Apple Silicon Metal)

Workflow

The script runs these steps in order. Each step prints a ==> step name header.

Prerequisite check. Verifies cargo, cmake, clang, ffmpeg, and yt-dlp are on PATH. Exits with install hints if any are missing.
Build. Runs cargo build --release in src/whisper-transcribe/. Skipped when the installed binary is newer than all source files (idempotent). Copies target/release/whisper-transcribe to ~/.local/bin/.
Model download. Downloads ggml-<model>.bin from HuggingFace via curl. Skipped when the model file already exists.
Smoke tests. Always run — verifies the install produces correct output, not just that it built.
- English: fetches samples/jfk.wav from the whisper.cpp upstream repository; checks transcript contains ask and country.
- Japanese: generates a clip via say -v Kyoko and converts with ffmpeg; checks transcript contains 天気 and 明日.

Prerequisites

The script checks and reports all missing tools before doing any work:

cargo — Rust toolchain (install via curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh)
cmake — required by whisper-sys build script (brew install cmake on macOS)
clang — C++ compiler for whisper.cpp FFI (xcode-select --install on macOS)
ffmpeg — audio transcoding for local transcription (brew install ffmpeg)
yt-dlp — YouTube download for the transcribe-youtube skill (brew install yt-dlp)

cmake and clang are toolchain dependencies — the user authors only Rust code; the C++ compilation happens inside cargo build via whisper-sys.

Disk

Build artifacts under src/whisper-transcribe/target/: ~400 MB (in .gitignore)
Default model ggml-large-v3-turbo.bin: ~809 MB
Plan for ~1.2 GB total on first setup; subsequent runs are near instant.

After setup

Binary: ~/.local/bin/whisper-transcribe
Model: ~/.local/share/whisper-transcribe/models/ggml-large-v3-turbo.bin

Use /connect0459-agent-skills:youtube-transcribe-local for local file transcription, or /connect0459-agent-skills:youtube-transcribe-transcribe-youtube for the full YouTube workflow.

---name: whisper-transcribe-setupdescription: Build the whisper-transcribe Rust binary from source and download the default Whisper model (~809 MB, ggml-large-v3-turbo). Installs the binary to ~/.local/bin/whisper-transcribe and the model to ~/.local/share/whisper-transcribe/models/. Runs English and Japanese smoke tests to verify the install works end-to-end. Trigger whenever the user wants to set up whisper transcription, install the whisper backend, build the Rust binary, download the Whisper model, or prepare for local or YouTube transcription — including phrases like "set up whisper", "install whisper", "build whisper-transcribe", "I need to transcribe audio", "prepare for transcription", or any time the user is missing the binary or model required by youtube-transcribe-local or youtube-transcribe-transcribe-youtube. Idempotent: safe to re-run. Build skips when the binary is current; model download skips when the file already exists; smoke tests always run.---# whisper-transcribe-setupBuild the `whisper-transcribe` Rust binary from `src/whisper-transcribe/` in this repository and download the default Whisper model. The binary and model are the shared backend for `/connect0459-agent-skills:youtube-transcribe-local` and `/connect0459-agent-skills:youtube-transcribe-transcribe-youtube`.## Script```bash<skill-path>/scripts/setup [--model NAME] [--no-smoke-test]```Defaults:- Binary install path: `~/.local/bin/whisper-transcribe`- Model directory: `~/.local/share/whisper-transcribe/models/`- Model: `large-v3-turbo` (~809 MB quantised, multilingual, fast on Apple Silicon Metal)## WorkflowThe script runs these steps in order. Each step prints a `==> step name` header.1. **Prerequisite check.** Verifies `cargo`, `cmake`, `clang`, `ffmpeg`, and `yt-dlp` are on PATH. Exits with install hints if any are missing.2. **Build.** Runs `cargo build --release` in `src/whisper-transcribe/`. Skipped when the installed binary is newer than all source files (idempotent). Copies `target/release/whisper-transcribe` to `~/.local/bin/`.3. **Model download.** Downloads `ggml-<model>.bin` from HuggingFace via `curl`. Skipped when the model file already exists.4. **Smoke tests.** Always run — verifies the install produces correct output, not just that it built. - **English**: fetches `samples/jfk.wav` from the whisper.cpp upstream repository; checks transcript contains `ask` and `country`. - **Japanese**: generates a clip via `say -v Kyoko` and converts with `ffmpeg`; checks transcript contains `天気` and `明日`.## PrerequisitesThe script checks and reports all missing tools before doing any work:- `cargo` — Rust toolchain (install via `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh`)- `cmake` — required by `whisper-sys` build script (`brew install cmake` on macOS)- `clang` — C++ compiler for whisper.cpp FFI (`xcode-select --install` on macOS)- `ffmpeg` — audio transcoding for local transcription (`brew install ffmpeg`)- `yt-dlp` — YouTube download for the transcribe-youtube skill (`brew install yt-dlp`)`cmake` and `clang` are toolchain dependencies — the user authors only Rust code; the C++ compilation happens inside `cargo build` via `whisper-sys`.## Disk- Build artifacts under `src/whisper-transcribe/target/`: ~400 MB (in `.gitignore`)- Default model `ggml-large-v3-turbo.bin`: ~809 MB- Plan for ~1.2 GB total on first setup; subsequent runs are near instant.## After setup- Binary: `~/.local/bin/whisper-transcribe`- Model: `~/.local/share/whisper-transcribe/models/ggml-large-v3-turbo.bin`Use `/connect0459-agent-skills:youtube-transcribe-local` for local file transcription, or `/connect0459-agent-skills:youtube-transcribe-transcribe-youtube` for the full YouTube workflow.

Frequently asked questions