Agent skill · software engineering · jeremylongshore
groq-core-workflow-b
Use when you need Groq's non-chat endpoints — transcribing or translating audio with Whisper, understanding images with Llama 4 vision, generating speech (TTS), or benchmarking models for speed vs quality. Trigger with phrases like "groq whisper", "groq transcription", "groq audio", "groq vision", "groq TTS", "groq speech".
Why this skill is useful
Adds executable scripts for audio transcription, translation, vision processing, and TTS that extend the AI's capabilities significantly.
What it needs
Requires groq-sdk installed locally. Requires groq account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Groq Core Workflow B: Audio, Vision & Speech Overview Beyond chat completions, Groq offers ultra-fast Whisper transcription (216x real-time), Llama 4 vision, and text-to-speech — all on the same groq-sdk client. This skill covers transcription/translation, vision, TTS, and model benchmarking, with full runnable code in references/implementation.md and worked scripts in references/examples.md. Prerequisites groq-sdk installed, GROQAPIKEY set (the SDK reads it from the environment automatically) For audio: audio files in a supported format For vision: image URLs or base64-encoded images Audio Models Model ID Languages Speed Best For ---------- ----------- ------- ---------- whisper-large-v3 100+ 164x real-time Best accuracy, multilingual whisper-large-v3-turbo 100+ 216x real-time Best speed/accuracy balance Supported audio formats: flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, webm Instructions Each workflow is a single SDK call on the shared groq client. Pick the endpoint for your task, then follow the full walkthrough in references/implementation.md for the complete, copy-pasteable version of each. 1. Transcription — groq.audio.transcriptions.create({ file, model: "whisper-large-v3-turbo", responseformat }). Use responseformat: "verbosejson" with timestampgranularities: ["segment"] to get per-segment start/end times. 2. Translation — groq.audio.translations.create({ file, model: "whisper-large-v3" }) transcribes any-language audio directly to English text. 3. Vision — a normal groq.chat.completions.create call where content is an array mixing { type: "text" } and { type: "imageurl" } parts. Accepts up to 5 images (URL or data: base64) with meta-llama/llama-4-scout-17b-16e-instruct. 4. Text-to-Speech — groq.audio.speech.create({ model: "playai-tts", input, voice, responseformat }), then write Buffer.from(await response.arrayBuffer()) to a file. 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/groq-core-workflow-b