Agent skill

Deepgram Core Workflow A

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Implement production pre-recorded speech-to-text with Deepgram.

MITAuto-check passedMedia & Creative

Install Deepgram Core Workflow A

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-core-workflow-a -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace deepgram-core-workflow-a --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/deepgram-core-workflow-a .claude/skills/deepgram-core-workflow-a && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deepgram-core-workflow-a
GitHub stars
2.8k
Token cost
~2.2k tokens
SKILL.md length
259 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Implement production pre-recorded speech-to-text with Deepgram.

  • Works in 5 steps: Transcription Service Class → Extract Structured Results → Batch Processing → …
  • Building audio transcription
  • SKILL.md covers Examples, Overview, Prerequisites and Instructions, plus 4 more sections
  • Calls npm and ffmpeg; needs DEEPGRAM_API_KEY

What it does

Deepgram Core Workflow A is an agent skill from jeremylongshore/tons-of-skills-marketplace. Implement production pre-recorded speech-to-text with Deepgram. Use when building audio transcription, batch processing, or implementing diarization and intelligence features. Trigger: "deepgram transcription", "speech to text", "transcribe audio", "batch transcription", "deepgram nova", "diarize audio".

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in Media & Creative, covering Transcription. It works with Deepgram. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Building audio transcription
  • Batch processing
  • Implementing diarization and intelligence features

Example prompts

  • “deepgram transcription”
  • “speech to text”
  • “transcribe audio”
  • “/deepgram-core-workflow-a”

Requirements

  • Node.js
  • A credential in DEEPGRAM_API_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*), Bash(pip:*), Grep

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Transcription Service Class
  2. Extract Structured Results
  3. Batch Processing
  4. Async Callback Transcription (Large Files)
  5. Keyword Boosting

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npm:*)
    • Bash(pip:*)
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • ffmpeg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • developers.deepgram.com
    • deepgram.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DEEPGRAM_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Deepgram Core Workflow A loads about 2.2k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 83 tokens; SKILL.md has 259 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 259 words, ~2,177 tokens.

Download SKILL.mdSave it as .claude/skills/deepgram-core-workflow-a/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
deepgram-core-workflow-a
description
Implement production pre-recorded speech-to-text with Deepgram. Use when building audio transcription, batch processing, or implementing diarization and intelligence features. Trigger: "deepgram transcription", "speech to text", "transcribe audio", "batch transcription", "deepgram nova", "diarize audio".
allowed-tools
Read, Write, Edit, Bash(npm:*), Bash(pip:*), Grep
compatibility
Designed for Claude Code
version
1.13.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, deepgram, voice-ai, transcription, workflow, stt

Deepgram Core Workflow A: Pre-recorded Transcription

Examples

Upload a short synthetic or licensed audio fixture to the development workflow, validate transcript format, timestamps, language/model choice, and error handling, then remove the artifact according to retention policy. Do not use customer recordings for a tutorial or log raw transcript text when a correlation ID and aggregate result suffice.

Overview

Production pre-recorded transcription service using Deepgram's REST API. Covers transcribeUrl and transcribeFile, speaker diarization, audio intelligence (summarization, topic detection, sentiment, intent), batch processing with concurrency control, and callback-based async transcription for large files.

Prerequisites

  • @deepgram/sdk installed, DEEPGRAM_API_KEY configured
  • Audio files: WAV, MP3, FLAC, OGG, M4A, or WebM
  • For batch: p-limit package (npm install p-limit)

Instructions

Step 1: Transcription Service Class
typescript
import { createClient, DeepgramClient } from '@deepgram/sdk';
import { readFileSync } from 'fs';

interface TranscribeOptions {
  model?: 'nova-3' | 'nova-2' | 'nova-2-meeting' | 'nova-2-phonecall' | 'base';
  language?: string;
  diarize?: boolean;
  utterances?: boolean;
  paragraphs?: boolean;
  smart_format?: boolean;
  summarize?: boolean;      // Audio intelligence
  detect_topics?: boolean;  // Topic detection
  sentiment?: boolean;      // Sentiment analysis
  intents?: boolean;        // Intent recognition
  keywords?: string[];      // Keyword boosting: ["term:weight"]
  callback?: string;        // Async callback URL
}

class DeepgramTranscriber {
  private client: DeepgramClient;

  constructor(apiKey: string) {
    this.client = createClient(apiKey);
  }

  async transcribeUrl(url: string, opts: TranscribeOptions = {}) {
    const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
      { url },
      {
        model: opts.model ?? 'nova-3',
        language: opts.language ?? 'en',
        smart_format: opts.smart_format ?? true,
        diarize: opts.diarize ?? false,
        utterances: opts.utterances ?? false,
        paragraphs: opts.paragraphs ?? false,
        summarize: opts.summarize ? 'v2' : undefined,
        detect_topics: opts.detect_topics ?? false,
        sentiment: opts.sentiment ?? false,
        intents: opts.intents ?? false,
        keywords: opts.keywords,
        callback: opts.callback,
      }
    );
    if (error) throw new Error(`Transcription failed: ${error.message}`);
    return result;
  }

  async transcribeFile(filePath: string, opts: TranscribeOptions = {}) {
    const audio = readFileSync(filePath);
    const mimetype = this.detectMimetype(filePath);

    const { result, error } = await this.client.listen.prerecorded.transcribeFile(
      audio,
      {
        model: opts.model ?? 'nova-3',
        smart_format: opts.smart_format ?? true,
        mimetype,
        diarize: opts.diarize ?? false,
        utterances: opts.utterances ?? false,
        summarize: opts.summarize ? 'v2' : undefined,
        detect_topics: opts.detect_topics ?? false,
        sentiment: opts.sentiment ?? false,
      }
    );
    if (error) throw new Error(`File transcription failed: ${error.message}`);
    return result;
  }

  private detectMimetype(path: string): string {
    const ext = path.split('.').pop()?.toLowerCase();
    const map: Record<string, string> = {
      wav: 'audio/wav', mp3: 'audio/mpeg', flac: 'audio/flac',
      ogg: 'audio/ogg', m4a: 'audio/mp4', webm: 'audio/webm',
    };
    return map[ext ?? ''] ?? 'audio/wav';
  }
}
Step 2: Extract Structured Results
typescript
function formatResult(result: any) {
  const channel = result.results.channels[0];
  const alt = channel.alternatives[0];

  return {
    transcript: alt.transcript,
    confidence: alt.confidence,
    words: alt.words?.map((w: any) => ({
      word: w.word,
      start: w.start,
      end: w.end,
      confidence: w.confidence,
      speaker: w.speaker,       // Only if diarize: true
      punctuated_word: w.punctuated_word,
    })),
    // Speaker segments (requires utterances: true + diarize: true)
    utterances: result.results.utterances?.map((u: any) => ({
      speaker: u.speaker,
      text: u.transcript,
      start: u.start,
      end: u.end,
      confidence: u.confidence,
    })),
    // Audio intelligence results
    summary: result.results.summary?.short,
    topics: result.results.topics?.segments,
    sentiments: result.results.sentiments?.segments,
    intents: result.results.intents?.segments,
    metadata: {
      duration: result.metadata.duration,
      channels: result.metadata.channels,
      model: result.metadata.model_info,
      request_id: result.metadata.request_id,
    },
  };
}
Step 3: Batch Processing
typescript
import pLimit from 'p-limit';

async function batchTranscribe(
  files: string[],
  opts: TranscribeOptions = {},
  concurrency = 5
) {
  const transcriber = new DeepgramTranscriber(process.env.DEEPGRAM_API_KEY!);
  const limit = pLimit(concurrency);

  const results = await Promise.allSettled(
    files.map(file =>
      limit(async () => {
        const result = await transcriber.transcribeFile(file, opts);
        console.log(`Done: ${file} (${result.metadata.duration}s)`);
        return { file, result: formatResult(result) };
      })
    )
  );

  const succeeded = results.filter(r => r.status === 'fulfilled');
  const failed = results.filter(r => r.status === 'rejected');
  console.log(`Batch complete: ${succeeded.length} ok, ${failed.length} failed`);
  return results;
}
Step 4: Async Callback Transcription (Large Files)
typescript
// For files >2 hours or when you don't want to hold a connection open,
// use Deepgram's callback feature. Deepgram POSTs results to your URL.
async function submitAsync(audioUrl: string, callbackUrl: string) {
  const transcriber = new DeepgramTranscriber(process.env.DEEPGRAM_API_KEY!);

  // Deepgram returns a request_id immediately, processes in background
  const result = await transcriber.transcribeUrl(audioUrl, {
    model: 'nova-3',
    diarize: true,
    callback: callbackUrl,  // Your HTTPS endpoint
  });

  console.log('Submitted. Request ID:', result.metadata.request_id);
  // Deepgram will POST results to callbackUrl when done
  // Retries up to 10 times with 30s delay on failure
}
Step 5: Keyword Boosting
typescript
// Boost domain-specific terms for higher accuracy
const result = await transcriber.transcribeUrl(audioUrl, {
  model: 'nova-3',
  keywords: [
    'Kubernetes:1.5',    // Boost weight 1.0-2.0
    'PostgreSQL:1.5',
    'microservices:1.3',
  ],
});

Output

  • DeepgramTranscriber class with URL and file transcription
  • Structured result extraction with word-level timing, speakers, and intelligence
  • Batch processing with configurable concurrency via p-limit
  • Async callback pattern for large files
  • Keyword boosting for domain vocabulary

Error Handling

ErrorCauseSolution
400 Bad RequestInvalid audio formatVerify file header bytes (WAV: RIFF, MP3: 0xFFF3/0xFFFB)
413 Payload Too LargeFile exceeds limitUse callback URL for async processing
Empty transcriptNo speech in audioCheck audio volume, try alternatives: 3 for confidence
408 TimeoutLong file, sync modeSwitch to callback-based async
Low confidenceBackground noisePreprocess: ffmpeg -i input.wav -af "highpass=f=200,lowpass=f=3000" clean.wav

Resources

Next Steps

Proceed to deepgram-core-workflow-b for real-time streaming transcription.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/deepgram-core-workflow-a of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Deepgram Core Workflow A next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Deepgram Core Workflow A compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deepgram Core Workflow A this skilljeremylongshore/tons-of-skills-marketplace2.8k—~2.2kAutomated safety check: PassMIT
DeepgramAnil-matcha/awesome-muse-connectors1.3k—~857Automated safety check: PassMIT
9Router Speech-to-Textdecolua/9router31k—~914Automated safety check: PassMIT
Deepgram JS Audio Intelligencedeepgram/deepgram-js-sdk276—~1.5kAutomated safety check: PassMIT
Deepgram JS Speech To Textdeepgram/deepgram-js-sdk276—~1.8kAutomated safety check: PassMIT
Keirouter Sttmydisha/keirouter147—~680Automated safety check: PassMIT

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Works with

Questions about Deepgram Core Workflow A

What does Deepgram Core Workflow A do?

Implement production pre-recorded speech-to-text with Deepgram. Deepgram Core Workflow A is an agent skill from jeremylongshore/tons-of-skills-marketplace. Implement production pre-recorded speech-to-text with Deepgram.

When should I use Deepgram Core Workflow A?

Deepgram Core Workflow A fits situations like: building audio transcription; batch processing; implementing diarization and intelligence features.

How do I install Deepgram Core Workflow A in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-core-workflow-a -a claude-code`. Or copy the skill folder (skills/.curated/deepgram-core-workflow-a in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/deepgram-core-workflow-a in your project. Claude Code loads it when a task matches its description.

How do I install Deepgram Core Workflow A in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-core-workflow-a -a codex`. Or copy the skill folder (skills/.curated/deepgram-core-workflow-a in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/deepgram-core-workflow-a in your project. Codex loads it when a task matches its description.

Can I use Deepgram Core Workflow A in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-core-workflow-a -a cursor` (or -a -a, -a or -a for the others). To copy it by hand, put the folder in .cursor/skills/deepgram-core-workflow-a, .gemini/skills/deepgram-core-workflow-a, .github/skills/deepgram-core-workflow-a and .opencode/skills/deepgram-core-workflow-a in your project.

What does Deepgram Core Workflow A need to run?

Going by SKILL.md and its folder, Deepgram Core Workflow A needs the command-line tools its instructions call (npm and ffmpeg) and credentials named DEEPGRAM_API_KEY. Our summary lists: Node.js; A credential in DEEPGRAM_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*), Bash(pip:*), Grep. Compatibility (from SKILL.md): Designed for Claude Code.

Does Deepgram Core Workflow A access the network?

SKILL.md names 2 domains. As links in the text: developers.deepgram.com and deepgram.com. This is read from the text; nothing was executed.

Is Deepgram Core Workflow A safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deepgram Core Workflow A use?

Deepgram Core Workflow A is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deepgram Core Workflow A use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Deepgram Core Workflow A?

Skills that share tags, products or a category with Deepgram Core Workflow A: Deepgram (Anil-matcha/awesome-muse-connectors, 1.3k stars), 9Router Speech-to-Text (decolua/9router, 31k stars), Deepgram JS Audio Intelligence (deepgram/deepgram-js-sdk, 276 stars) and Deepgram JS Speech To Text (deepgram/deepgram-js-sdk, 276 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepgram Core Workflow A?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.