Use when writing or reviewing JavaScript/TypeScript in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, and entity detection. Same endpoint as plain STT, different params. Covers REST via `client.listen.v1.media.transcribeUrl` / `transcribeFile` and the WebSocket-supported subset on `client.listen.v1.createConnection()` / `connect()`. Use `deepgram-js-speech-to-text` for plain transcription and `deepgram-js-text-intelligence` for analytics on already-transcribed text. Triggers include "audio intelligence", "summarize audio", "diarize", "sentiment from audio", "redact PII", and "detect language audio".
npx skills add https://github.com/deepgram/deepgram-js-sdk --skill deepgram-js-audio-intelligence
Analytics overlays applied to /v1/listen: summaries, topics, intents, sentiment, language detection, diarization, redaction, entities. Same client surface as STT; turn features on with parameters.
Use a different skill when:
deepgram-js-speech-to-text.deepgram-js-text-intelligence.deepgram-js-conversational-stt.deepgram-js-voice-agent.| Feature | REST | WSS |
|---|---|---|
| diarize | yes | yes |
| redact | yes | yes |
| detect_entities | yes | yes |
| punctuate, smart_format | yes | yes |
| summarize | yes | no in current WSS connect args |
| topics | yes | no |
| intents | yes | no |
| sentiment | yes | no |
| detect_language | yes | no |
require("dotenv").config();
const { DeepgramClient } = require("@deepgram/sdk");
const deepgramClient = new DeepgramClient({
apiKey: process.env.DEEPGRAM_API_KEY,
});
From examples/22-transcription-advanced-options.ts:
const data = await deepgramClient.listen.v1.media.transcribeUrl({
url: "https://dpgr.am/spacewalk.wav",
model: "nova-3",
language: "en",
punctuate: true,
paragraphs: true,
utterances: true,
smart_format: true,
sentiment: true,
topics: true,
custom_topic: "custom_topic",
custom_topic_mode: "extended",
intents: true,
custom_intent: "custom_intent",
custom_intent_mode: "extended",
detect_entities: true,
detect_language: true,
diarize: true,
keyterm: ["keyword1", "keyword2"],
redact: ["pci", "ssn"],
});
Start from examples/07-transcription-live-websocket.ts and keep the same socket flow, but only use WSS-supported intelligence flags such as diarize, redact, and detect_entities in the connection args.
const deepgramConnection = await deepgramClient.listen.v1.createConnection({
model: "nova-3",
diarize: true,
redact: "pci",
detect_entities: true,
});
summarize, topics, intents, sentiment, detect_language, detect_entities, diarize, redact, custom_topic, custom_topic_mode, custom_intent, custom_intent_mode.model, language, encoding, sample_rate, punctuate, smart_format, utterances, paragraphs, multichannel.keyterm, not keywords.reference.md → Listen V1 Media; WSS subset behavior lives in src/CustomClient.ts and src/api/resources/listen/resources/v1/client/{Client,Socket}.ts./llmstxt/developers_deepgram_llms_txtsummarize on /v1/listen is versioned, not plain boolean. The generated REST surface and examples point at "v2".topics, intents, sentiment, summarize, or detect_language.redact typing is looser in practice than in the generated alias. Examples pass arrays like ["pci", "ssn"], even though ListenV1Redact itself is just a string alias.keyterm for Nova-3 biasing. examples/22-transcription-advanced-options.ts explicitly notes keywords are not supported for Nova-3.nova-3 is the safest choice when mixing many overlays.examples/22-transcription-advanced-options.tsexamples/04-transcription-prerecorded-url.tsexamples/05-transcription-prerecorded-file.tsexamples/07-transcription-live-websocket.tsFor cross-language Deepgram product knowledge — the consolidated API reference, documentation finder, focused runnable recipes, third-party integration examples, and MCP setup — install the central skills:
npx skills add deepgram/skills
This SDK ships language-idiomatic code skills; deepgram/skills ships cross-language product knowledge (see api, docs, recipes, examples, starters, setup-mcp).
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take deepgram/deepgram-js-audio-intelligence from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npx.
Without those the skill loads but fails at the first command.