Agent skill

Deepgram Observability

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

Set up comprehensive observability for Deepgram integrations.

MITAuto-check passedDevOps & Cloud

Install Deepgram Observability

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

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

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

At a glance

Set up comprehensive observability for Deepgram integrations.

  • Works in 6 steps: Prometheus Metrics Definition → Instrumented Deepgram Client → OpenTelemetry Tracing → …
  • Implementing monitoring
  • SKILL.md covers Prerequisites, Examples, Overview and Four Pillars, plus 5 more sections
  • Needs DEEPGRAM_API_KEY

What it does

Deepgram Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace. Set up comprehensive observability for Deepgram integrations. Use when implementing monitoring, setting up dashboards, or configuring alerting for Deepgram integration health. Trigger: "deepgram monitoring", "deepgram metrics", "deepgram observability", "monitor deepgram", "deepgram alerts", "deepgram dashboard".

Its SKILL.md is about 3k 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 Observability and Monitoring and alerting. It works with Deepgram, Prometheus and OpenTelemetry. 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

  • Implementing monitoring
  • Setting up dashboards
  • Configuring alerting for Deepgram integration health

Example prompts

  • “deepgram monitoring”
  • “deepgram metrics”
  • “deepgram observability”
  • “/deepgram-observability”

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(curl:*)

Workflow steps

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

  1. Prometheus Metrics Definition
  2. Instrumented Deepgram Client
  3. OpenTelemetry Tracing
  4. Structured Logging with Pino
  5. Grafana Dashboard Panels
  6. AlertManager Rules

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(curl:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript, json and yaml).

    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):

    • github.com
    • opentelemetry.io
    • getpino.io
    • grafana.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 Observability loads about 3k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 258 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

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). 258 words, ~2,978 tokens.

Download SKILL.mdSave it as .claude/skills/deepgram-observability/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
deepgram-observability
description
Set up comprehensive observability for Deepgram integrations. Use when implementing monitoring, setting up dashboards, or configuring alerting for Deepgram integration health. Trigger: "deepgram monitoring", "deepgram metrics", "deepgram observability", "monitor deepgram", "deepgram alerts", "deepgram dashboard".
allowed-tools
Read, Write, Edit, Bash(curl:*)
compatibility
Designed for Claude Code
version
1.13.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, deepgram, monitoring, observability, prometheus

Deepgram Observability

Prerequisites

  • An approved metrics/tracing/logging backend, data-retention policy, and on-call owner.
  • Redaction rules that exclude audio, transcript text, API keys, participant identifiers, and other sensitive metadata.

Examples

Emit aggregate metrics for request count, latency, model, status class, streaming duration, and rate-limit headroom. Trigger a non-sensitive staging failure to verify alert routing, then record the correlation ID and remediation time—never audio samples, transcripts, or credentials.

Overview

Full observability stack for Deepgram: Prometheus metrics (request counts, latency histograms, audio processed, cost tracking), OpenTelemetry distributed tracing, structured JSON logging with Pino, Grafana dashboard JSON, and AlertManager rules.

Four Pillars

PillarToolWhat It Tracks
MetricsPrometheusRequest rate, latency, error rate, audio minutes, estimated cost
TracesOpenTelemetryEnd-to-end request flow, Deepgram API span timing
LogsPino (JSON)Request details, errors, audit trail
AlertsAlertManagerError rate >5%, P95 latency >10s, rate limit hits

Instructions

Step 1: Prometheus Metrics Definition
typescript
import { Counter, Histogram, Gauge, Registry, collectDefaultMetrics } from 'prom-client';

const registry = new Registry();
collectDefaultMetrics({ register: registry });

// Request metrics
const requestsTotal = new Counter({
  name: 'deepgram_requests_total',
  help: 'Total Deepgram API requests',
  labelNames: ['method', 'model', 'status'] as const,
  registers: [registry],
});

const latencyHistogram = new Histogram({
  name: 'deepgram_request_duration_seconds',
  help: 'Deepgram API request duration',
  labelNames: ['method', 'model'] as const,
  buckets: [0.1, 0.5, 1, 2, 5, 10, 30, 60],
  registers: [registry],
});

// Usage metrics
const audioProcessedSeconds = new Counter({
  name: 'deepgram_audio_processed_seconds_total',
  help: 'Total audio seconds processed',
  labelNames: ['model'] as const,
  registers: [registry],
});

const estimatedCostDollars = new Counter({
  name: 'deepgram_estimated_cost_dollars_total',
  help: 'Estimated cost in USD',
  labelNames: ['model', 'method'] as const,
  registers: [registry],
});

// Operational metrics
const activeConnections = new Gauge({
  name: 'deepgram_active_websocket_connections',
  help: 'Currently active WebSocket connections',
  registers: [registry],
});

const rateLimitHits = new Counter({
  name: 'deepgram_rate_limit_hits_total',
  help: 'Number of 429 rate limit responses',
  registers: [registry],
});

export { registry, requestsTotal, latencyHistogram, audioProcessedSeconds,
         estimatedCostDollars, activeConnections, rateLimitHits };
Step 2: Instrumented Deepgram Client
typescript
import { createClient, DeepgramClient } from '@deepgram/sdk';

class InstrumentedDeepgram {
  private client: DeepgramClient;
  private costPerMinute: Record<string, number> = {
    'nova-3': 0.0043, 'nova-2': 0.0043, 'base': 0.0048, 'whisper-large': 0.0048,
  };

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

  async transcribeUrl(url: string, options: Record<string, any> = {}) {
    const model = options.model ?? 'nova-3';
    const timer = latencyHistogram.startTimer({ method: 'prerecorded', model });

    try {
      const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
        { url }, { model, smart_format: true, ...options }
      );

      const status = error ? 'error' : 'success';
      timer();
      requestsTotal.inc({ method: 'prerecorded', model, status });

      if (error) {
        if ((error as any).status === 429) rateLimitHits.inc();
        throw error;
      }

      // Track usage
      const duration = result.metadata.duration;
      audioProcessedSeconds.inc({ model }, duration);
      estimatedCostDollars.inc(
        { model, method: 'prerecorded' },
        (duration / 60) * (this.costPerMinute[model] ?? 0.0043)
      );

      return result;
    } catch (err) {
      timer();
      requestsTotal.inc({ method: 'prerecorded', model, status: 'error' });
      throw err;
    }
  }

  // Live transcription with connection tracking
  connectLive(options: Record<string, any>) {
    const model = options.model ?? 'nova-3';
    activeConnections.inc();

    const connection = this.client.listen.live(options);

    const originalFinish = connection.finish.bind(connection);
    connection.finish = () => {
      activeConnections.dec();
      return originalFinish();
    };

    return connection;
  }
}
Step 3: OpenTelemetry Tracing
typescript
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
import { Resource } from '@opentelemetry/resources';
import { SEMRESATTRS_SERVICE_NAME } from '@opentelemetry/semantic-conventions';
import { trace } from '@opentelemetry/api';

const sdk = new NodeSDK({
  resource: new Resource({
    [SEMRESATTRS_SERVICE_NAME]: 'deepgram-service',
    'deployment.environment': process.env.NODE_ENV ?? 'development',
  }),
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? 'http://localhost:4318/v1/traces',
  }),
  instrumentations: [
    getNodeAutoInstrumentations({
      '@opentelemetry/instrumentation-http': {
        ignoreIncomingPaths: ['/health', '/metrics'],
      },
    }),
  ],
});

sdk.start();

// Add custom spans for Deepgram operations
const tracer = trace.getTracer('deepgram');

async function tracedTranscribe(url: string, model: string) {
  return tracer.startActiveSpan('deepgram.transcribe', async (span) => {
    span.setAttribute('deepgram.model', model);
    span.setAttribute('deepgram.audio_url', url.substring(0, 100));

    try {
      const instrumented = new InstrumentedDeepgram(process.env.DEEPGRAM_API_KEY!);
      const result = await instrumented.transcribeUrl(url, { model });

      span.setAttribute('deepgram.duration_seconds', result.metadata.duration);
      span.setAttribute('deepgram.request_id', result.metadata.request_id);
      span.setAttribute('deepgram.confidence',
        result.results.channels[0].alternatives[0].confidence);

      return result;
    } catch (err: any) {
      span.recordException(err);
      span.setStatus({ code: 2, message: err.message });
      throw err;
    } finally {
      span.end();
    }
  });
}
Step 4: Structured Logging with Pino
typescript
import pino from 'pino';

const logger = pino({
  level: process.env.LOG_LEVEL ?? 'info',
  formatters: {
    level: (label) => ({ level: label }),
  },
  timestamp: pino.stdTimeFunctions.isoTime,
  base: {
    service: 'deepgram-integration',
    env: process.env.NODE_ENV,
  },
});

// Child loggers per component
const transcriptionLog = logger.child({ component: 'transcription' });
const metricsLog = logger.child({ component: 'metrics' });

// Usage:
transcriptionLog.info({
  action: 'transcribe',
  model: 'nova-3',
  audioUrl: url.substring(0, 100),
  requestId: result.metadata.request_id,
  duration: result.metadata.duration,
  confidence: result.results.channels[0].alternatives[0].confidence,
}, 'Transcription completed');

transcriptionLog.error({
  action: 'transcribe',
  model: 'nova-3',
  error: err.message,
  statusCode: err.status,
}, 'Transcription failed');
Step 5: Grafana Dashboard Panels
json
{
  "title": "Deepgram Observability",
  "panels": [
    {
      "title": "Request Rate",
      "type": "timeseries",
      "targets": [{ "expr": "rate(deepgram_requests_total[5m])" }]
    },
    {
      "title": "P95 Latency",
      "type": "gauge",
      "targets": [{ "expr": "histogram_quantile(0.95, rate(deepgram_request_duration_seconds_bucket[5m]))" }]
    },
    {
      "title": "Error Rate %",
      "type": "stat",
      "targets": [{ "expr": "rate(deepgram_requests_total{status='error'}[5m]) / rate(deepgram_requests_total[5m]) * 100" }]
    },
    {
      "title": "Audio Processed (min/hr)",
      "type": "timeseries",
      "targets": [{ "expr": "rate(deepgram_audio_processed_seconds_total[1h]) / 60" }]
    },
    {
      "title": "Estimated Daily Cost",
      "type": "stat",
      "targets": [{ "expr": "increase(deepgram_estimated_cost_dollars_total[24h])" }]
    },
    {
      "title": "Active WebSocket Connections",
      "type": "gauge",
      "targets": [{ "expr": "deepgram_active_websocket_connections" }]
    }
  ]
}
Step 6: AlertManager Rules
yaml
groups:
  - name: deepgram-alerts
    rules:
      - alert: DeepgramHighErrorRate
        expr: >
          rate(deepgram_requests_total{status="error"}[5m])
          / rate(deepgram_requests_total[5m]) > 0.05
        for: 5m
        labels: { severity: critical }
        annotations:
          summary: "Deepgram error rate > 5% for 5 minutes"

      - alert: DeepgramHighLatency
        expr: >
          histogram_quantile(0.95,
            rate(deepgram_request_duration_seconds_bucket[5m])
          ) > 10
        for: 5m
        labels: { severity: warning }
        annotations:
          summary: "Deepgram P95 latency > 10 seconds"

      - alert: DeepgramRateLimited
        expr: rate(deepgram_rate_limit_hits_total[1h]) > 10
        for: 10m
        labels: { severity: warning }
        annotations:
          summary: "Deepgram rate limit hits > 10/hour"

      - alert: DeepgramCostSpike
        expr: >
          increase(deepgram_estimated_cost_dollars_total[24h])
          > 2 * increase(deepgram_estimated_cost_dollars_total[24h] offset 1d)
        for: 30m
        labels: { severity: warning }
        annotations:
          summary: "Deepgram daily cost > 2x yesterday"

      - alert: DeepgramZeroRequests
        expr: rate(deepgram_requests_total[15m]) == 0
        for: 15m
        labels: { severity: warning }
        annotations:
          summary: "No Deepgram requests for 15 minutes"

Metrics Endpoint

typescript
import express from 'express';
const app = express();

app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});

Output

  • Prometheus metrics (6 metrics covering requests, latency, usage, cost)
  • Instrumented Deepgram client with auto-tracking
  • OpenTelemetry distributed tracing with custom spans
  • Structured JSON logging (Pino)
  • Grafana dashboard panel definitions
  • AlertManager rules (5 alerts)

Error Handling

IssueCauseSolution
Metrics not appearingRegistry not exportedCheck /metrics endpoint
High cardinalityToo many label valuesLimit labels to known set
Alert stormsThresholds too sensitiveAdd for: duration, tune values
Missing tracesOTEL exporter not configuredSet OTEL_EXPORTER_OTLP_ENDPOINT

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-observability of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

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Categories

Questions about Deepgram Observability

What does Deepgram Observability do?

Set up comprehensive observability for Deepgram integrations. Deepgram Observability is an agent skill from jeremylongshore/tons-of-skills-marketplace. Set up comprehensive observability for Deepgram integrations.

When should I use Deepgram Observability?

Deepgram Observability fits situations like: implementing monitoring; setting up dashboards; configuring alerting for Deepgram integration health.

How do I install Deepgram Observability in Claude Code?

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

How do I install Deepgram Observability in Codex?

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

Can I use Deepgram Observability 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-observability -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-observability, .gemini/skills/deepgram-observability, .github/skills/deepgram-observability and .opencode/skills/deepgram-observability in your project.

What does Deepgram Observability need to run?

Going by SKILL.md and its folder, Deepgram Observability needs 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(curl:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Deepgram Observability access the network?

SKILL.md names 4 domains. As links in the text: github.com, opentelemetry.io, getpino.io and grafana.com. This is read from the text; nothing was executed.

Is Deepgram Observability 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 Observability use?

Deepgram Observability 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 Observability use?

About 3k tokens (SKILL.md is roughly 12k 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 2k tokens, read only when the agent opens those files.

What are the alternatives to Deepgram Observability?

Skills that share tags, products or a category with Deepgram Observability: Developing Funboost Mixin (ydf0509/funboost, 895 stars), Archestra Dev Observability (archestra-ai/archestra, 4.4k stars), Frontmcp Observability (agentfront/frontmcp, 146 stars) and Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepgram Observability?

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.