Agent skill

Deepgram Deploy Integration

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

Deploy Deepgram integrations to production environments. An agent skill from jeremylongshore/tons-of-skills-marketplace.

MITAuto-check passedDevOps & Cloud

Install Deepgram Deploy Integration

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

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace deepgram-deploy-integration --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-deploy-integration .claude/skills/deepgram-deploy-integration && 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-deploy-integration
GitHub stars
2.8k
Token cost
~2.5k tokens
SKILL.md length
238 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Deploy Deepgram integrations to production environments. An agent skill from jeremylongshore/tons-of-skills-marketplace.

  • Works in 6 steps: Production Dockerfile → Docker Compose → Kubernetes Deployment → …
  • Deploying to cloud platforms
  • SKILL.md covers Examples, Overview, Prerequisites and Instructions, plus 3 more sections
  • Calls kubectl, npm and gcloud; needs DEEPGRAM_API_KEY

What it does

Deepgram Deploy Integration is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy Deepgram integrations to production environments. Use when deploying to cloud platforms, configuring containers, or setting up Deepgram in Docker/Kubernetes/serverless. Trigger: "deploy deepgram", "deepgram docker", "deepgram kubernetes", "deepgram production deploy", "deepgram cloud run", "deepgram lambda".

Its SKILL.md is about 2.5k 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 DevOps & Cloud, covering Containers, Container orchestration and Serverless. It works with Deepgram, Docker, Kubernetes and Cloud Run. 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

  • Deploying to cloud platforms
  • Configuring containers
  • Setting up Deepgram in Docker/Kubernetes/serverless

Example prompts

  • “deploy deepgram”
  • “deepgram docker”
  • “deepgram kubernetes”
  • “/deepgram-deploy-integration”

Requirements

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

Workflow steps

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

  1. Production Dockerfile
  2. Docker Compose
  3. Kubernetes Deployment
  4. AWS Lambda Handler
  5. Google Cloud Run
  6. Deploy Script

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(docker:*)
    • Bash(kubectl:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • kubectl
    • npm
    • gcloud
    • docker
    • curl

    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):

    • docs.docker.com
    • kubernetes.io
    • docs.aws.amazon.com
    • cloud.google.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 Deploy Integration loads about 2.5k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 238 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4k

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). 238 words, ~2,524 tokens.

Download SKILL.mdSave it as .claude/skills/deepgram-deploy-integration/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
deepgram-deploy-integration
description
Deploy Deepgram integrations to production environments. Use when deploying to cloud platforms, configuring containers, or setting up Deepgram in Docker/Kubernetes/serverless. Trigger: "deploy deepgram", "deepgram docker", "deepgram kubernetes", "deepgram production deploy", "deepgram cloud run", "deepgram lambda".
allowed-tools
Read, Write, Edit, Bash(docker:*), Bash(kubectl:*)
compatibility
Designed for Claude Code
version
1.13.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, deepgram, deployment, docker, kubernetes, serverless

Deepgram Deploy Integration

Examples

Deploy a versioned integration to staging with a scoped secret reference and a short licensed fixture, then verify health, timeout/retry behavior, redacted metrics, and the rollback command. Promote through a controlled production canary only after data/consent and quality checks pass; do not deploy a key or audio fixture in the manifest.

Overview

Deploy Deepgram transcription services to Docker, Kubernetes, AWS Lambda, and Google Cloud Run. Includes production Dockerfile, K8s manifests with secret management, serverless handlers for event-driven transcription, and health check patterns.

Prerequisites

  • Working Deepgram integration (tested locally)
  • Production API key in secret manager
  • Container registry access (Docker Hub, ECR, GCR)
  • Target platform CLI installed

Instructions

Step 1: Production Dockerfile
dockerfile
# Multi-stage build for minimal production image
FROM node:20-alpine AS builder

WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY tsconfig.json ./
COPY src/ ./src/
RUN npm run build

FROM node:20-alpine AS runtime

# Security: non-root user
RUN addgroup -g 1001 -S app && adduser -S app -u 1001
WORKDIR /app

# Production dependencies only
COPY package*.json ./
RUN npm ci --production && npm cache clean --force

# Copy built application
COPY --from=builder /app/dist ./dist

# Health check (tests Deepgram connectivity)
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
  CMD wget -q --spider http://localhost:3000/health || exit 1

USER app
EXPOSE 3000

CMD ["node", "dist/server.js"]
Step 2: Docker Compose
yaml
# docker-compose.yml
version: '3.8'

services:
  deepgram-service:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
      - DEEPGRAM_API_KEY=${DEEPGRAM_API_KEY}
      - DEEPGRAM_MODEL=nova-3
    healthcheck:
      test: ["CMD", "wget", "-q", "--spider", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
    restart: unless-stopped
    deploy:
      resources:
        limits:
          memory: 512M
          cpus: '1.0'

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data

volumes:
  redis-data:
Step 3: Kubernetes Deployment
yaml
# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: deepgram-service
  labels:
    app: deepgram-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: deepgram-service
  template:
    metadata:
      labels:
        app: deepgram-service
    spec:
      containers:
        - name: deepgram-service
          image: your-registry/deepgram-service:latest
          ports:
            - containerPort: 3000
          env:
            - name: NODE_ENV
              value: production
            - name: DEEPGRAM_API_KEY
              valueFrom:
                secretKeyRef:
                  name: deepgram-secrets
                  key: api-key
            - name: DEEPGRAM_MODEL
              value: nova-3
          resources:
            requests:
              memory: "256Mi"
              cpu: "250m"
            limits:
              memory: "512Mi"
              cpu: "1000m"
          livenessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 10
            periodSeconds: 30
          readinessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: deepgram-service
spec:
  selector:
    app: deepgram-service
  ports:
    - port: 80
      targetPort: 3000
  type: ClusterIP
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: deepgram-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: deepgram-service
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
bash
# Create secret
kubectl create secret generic deepgram-secrets \
  --from-literal=api-key=$DEEPGRAM_API_KEY

# Deploy
kubectl apply -f k8s/
Step 4: AWS Lambda Handler
typescript
// lambda/handler.ts
import { createClient } from '@deepgram/sdk';
import { S3Client, GetObjectCommand } from '@aws-sdk/client-s3';
import type { S3Event } from 'aws-lambda';

const deepgram = createClient(process.env.DEEPGRAM_API_KEY!);
const s3 = new S3Client({});

// Trigger: S3 upload of audio file -> Lambda -> Deepgram -> Store result
export async function handler(event: S3Event) {
  for (const record of event.Records) {
    const bucket = record.s3.bucket.name;
    const key = decodeURIComponent(record.s3.object.key);

    console.log(`Processing: s3://${bucket}/${key}`);

    // Get audio from S3
    const { Body } = await s3.send(new GetObjectCommand({ Bucket: bucket, Key: key }));
    const audio = Buffer.from(await Body!.transformToByteArray());

    // Transcribe
    const { result, error } = await deepgram.listen.prerecorded.transcribeFile(
      audio,
      {
        model: 'nova-3',
        smart_format: true,
        diarize: true,
        utterances: true,
      }
    );

    if (error) {
      console.error(`Transcription failed for ${key}:`, error.message);
      throw error;
    }

    console.log(`Transcribed ${key}: ${result.metadata.duration}s, ` +
      `${result.results.channels[0].alternatives[0].words?.length} words`);

    return {
      statusCode: 200,
      body: JSON.stringify({
        file: key,
        duration: result.metadata.duration,
        transcript: result.results.channels[0].alternatives[0].transcript,
        request_id: result.metadata.request_id,
      }),
    };
  }
}
Step 5: Google Cloud Run
typescript
// server.ts — Cloud Run entry point
import express from 'express';
import { createClient } from '@deepgram/sdk';

const app = express();
app.use(express.json({ limit: '50mb' }));

const deepgram = createClient(process.env.DEEPGRAM_API_KEY!);

app.post('/transcribe', async (req, res) => {
  try {
    const { url, model = 'nova-3', diarize = false } = req.body;

    const { result, error } = await deepgram.listen.prerecorded.transcribeUrl(
      { url },
      { model, smart_format: true, diarize }
    );

    if (error) return res.status(502).json({ error: error.message });

    res.json({
      transcript: result.results.channels[0].alternatives[0].transcript,
      confidence: result.results.channels[0].alternatives[0].confidence,
      duration: result.metadata.duration,
      request_id: result.metadata.request_id,
    });
  } catch (err: any) {
    res.status(500).json({ error: err.message });
  }
});

app.get('/health', async (req, res) => {
  try {
    const { error } = await deepgram.manage.getProjects();
    res.json({ status: error ? 'degraded' : 'healthy' });
  } catch {
    res.status(503).json({ status: 'unhealthy' });
  }
});

const port = process.env.PORT || 3000;
app.listen(port, () => console.log(`Listening on port ${port}`));
bash
# Deploy to Cloud Run
gcloud run deploy deepgram-service \
  --source . \
  --set-env-vars DEEPGRAM_API_KEY=$(gcloud secrets versions access latest --secret deepgram-key) \
  --memory 512Mi \
  --timeout 300 \
  --concurrency 50 \
  --min-instances 1 \
  --max-instances 10
Step 6: Deploy Script
bash
#!/bin/bash
set -euo pipefail

ENV="${1:?Usage: deploy.sh <staging|production>}"

echo "Deploying to $ENV..."

# Build
npm ci && npm run build && npm test

# Build container
docker build -t deepgram-service:$ENV .

# Deploy based on target
case $ENV in
  staging)
    kubectl --context staging apply -f k8s/
    kubectl --context staging rollout status deployment/deepgram-service
    ;;
  production)
    kubectl --context production apply -f k8s/
    kubectl --context production rollout status deployment/deepgram-service
    ;;
esac

# Post-deploy smoke test
echo "Running smoke test..."
ENDPOINT=$(kubectl get svc deepgram-service -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
curl -sf "http://$ENDPOINT/health" || { echo "SMOKE TEST FAILED"; exit 1; }
echo "Deploy successful."

Output

  • Production Dockerfile (multi-stage, non-root, health check)
  • Docker Compose with Redis for caching
  • Kubernetes manifests (Deployment, Service, HPA, Secret)
  • AWS Lambda handler (S3 trigger -> Deepgram -> result)
  • Cloud Run service with health check
  • Environment-aware deploy script

Error Handling

IssueCauseSolution
Container OOMMemory limit too lowIncrease to 512Mi+
Health check failingService not ready yetIncrease initialDelaySeconds
Lambda timeoutAudio too longIncrease timeout to 300s, or use callback
Cloud Run 429Too many concurrent requestsDecrease --concurrency flag
Secret not foundK8s secret missingCreate secret before deploying

Resources

© 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-deploy-integration of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

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Devopsnicepkg/auto-company1952 repos~814Automated safety check: PassMIT
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Categories

Questions about Deepgram Deploy Integration

What does Deepgram Deploy Integration do?

Deploy Deepgram integrations to production environments. An agent skill from jeremylongshore/tons-of-skills-marketplace. Deepgram Deploy Integration is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy Deepgram integrations to production environments.

When should I use Deepgram Deploy Integration?

Deepgram Deploy Integration fits situations like: deploying to cloud platforms; configuring containers; setting up Deepgram in Docker/Kubernetes/serverless.

How do I install Deepgram Deploy Integration in Claude Code?

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

How do I install Deepgram Deploy Integration in Codex?

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

Can I use Deepgram Deploy Integration 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-deploy-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepgram-deploy-integration, .gemini/skills/deepgram-deploy-integration, .github/skills/deepgram-deploy-integration and .opencode/skills/deepgram-deploy-integration in your project.

What does Deepgram Deploy Integration need to run?

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

Does Deepgram Deploy Integration access the network?

SKILL.md names 4 domains. As links in the text: docs.docker.com, kubernetes.io, docs.aws.amazon.com and cloud.google.com. This is read from the text; nothing was executed.

Is Deepgram Deploy Integration 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 Deploy Integration use?

Deepgram Deploy Integration 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 Deploy Integration use?

About 2.5k tokens (SKILL.md is roughly 10k 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 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Deepgram Deploy Integration?

Skills that share tags, products or a category with Deepgram Deploy Integration: Discover Infra (rand/cc-polymath, 181 stars), LangBot Deployment Guide (langbot-app/LangBot, 18k stars), Devops (nicepkg/auto-company, 195 stars) and Debug Openshell Cluster (NVIDIA/OpenShell, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepgram Deploy Integration?

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.