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Deepgram

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Anil-matcha
deepgram

Deepgram speech AI: transcribe audio to text and synthesize speech (TTS). Trigger phrases: deepgram, transcribe audio, speech to text, STT, deepgram tts.

Overview

PublisherAnil-matcha
Repositoryawesome-muse-connectors
Skill namedeepgram
Stars
1.4K
Forks
309
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Anil-matcha on GitHub. Read the source before you install it.

Installation

Install the Deepgram AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/deepgram
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Anil-matcha/awesome-muse-connectors.git /tmp/awesome-muse-connectors
mkdir -p .claude/skills
cp -r /tmp/awesome-muse-connectors/connectors/deepgram .claude/skills/deepgram
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deepgram in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deepgram on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deepgram is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deepgram

Purpose

Transcribe prerecorded audio files to text (with optional diarization, summaries, topics, sentiment) and synthesize speech with Deepgram's Aura voices. Reach for this when the user has an audio file to transcribe or wants spoken audio generated from text.

Tooling

All commands go through bin/deepgram.py. Auth uses exactly Authorization: Token <api_key> (not Bearer); the CLI wires it through the credential surrogate.

bash
bin/deepgram.py auth
# {"ok": true, "projects": 2} on success

bin/deepgram.py projects
# list Deepgram projects (id, name)

bin/deepgram.py transcribe --file meeting.wav
# upload a local audio file, print the transcript JSON

bin/deepgram.py transcribe --file call.mp3 --model nova-3 --diarize --summarize --topics --sentiment
# transcript with speaker labels plus summary, topics and sentiment

bin/deepgram.py tts --text "Thanks for listening." --out thanks.mp3
# synthesize with an Aura voice to a local MP3 file; prints the saved path

Notes:

  • transcribe reads a local audio file, posts the bytes to POST /v1/listen, and prints the full transcript JSON. Content type is guessed from the file extension (wav, mp3, m4a, flac, ogg supported).
  • tts posts {"text": "..."} to POST /v1/speak?model=<model>&encoding=mp3 (default model aura-2-thalia-en, 2,000-char limit) and saves the audio to --out (default deepgram-output.mp3).
  • Live WebSocket STT (/v1/listen streaming) is real-time and outside CLI scope.

Auth

  • Provider id: deepgram (credential is collected as custom.deepgram)
  • Collection: Deepgram API key via the secure credential flow (credentials.request_api_access); create one in the Deepgram console
  • Allowed hosts: api.deepgram.com
  • Connect placement: custom_header:Authorization (the stored credential value must be exactly Token <api_key>, with the Token prefix; Bearer is not accepted)
  • Status check: bin/deepgram.py auth

Operating Rules

  1. Usage is metered by audio duration (transcription) and characters (TTS). Transcribe and synthesize only what the user asked for; confirm before processing large batches.
  2. API key and project management are confirmation-gated: confirm with the user before creating, rotating, or deleting keys or projects. This draft CLI is read/compute only; it does not manage keys or projects.
  3. The transcript JSON can contain full conversation text. Treat transcripts as the user's private content; do not republish or forward them without instruction.
  4. Never log or print the raw key; the CLI only ever handles the surrogate.

Files

  • SKILL.md
  • bin/deepgram.py

Maturity

🧪 Draft: written from Deepgram's public API docs with paths cross-checked at build time; not yet live-tested end-to-end. Key/project management and usage endpoints are not yet in the CLI.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deepgram AI skill do?

Deepgram speech AI: transcribe audio to text and synthesize speech (TTS). Trigger phrases: deepgram, transcribe audio, speech to text, STT, deepgram tts.

Why use Deepgram on TypingMind?

Because you install it once and use it with any model. Deepgram is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deepgram in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Anil-matcha/awesome-muse-connectors/tree/main/connectors/deepgram. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deepgram?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deepgram?

As many as you like. As long as a model supports skills, you can use Deepgram with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deepgram AI skill free?

Yes. It is published on GitHub by Anil-matcha under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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