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Monitoring Observability

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rohitg00
monitoring-observability

Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill namemonitoring-observability
Stars
2.6K
Forks
963
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Monitoring Observability 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.
  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/rohitg00/awesome-claude-code-toolkit.git /tmp/awesome-claude-code-toolkit
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code-toolkit/skills/monitoring-observability .claude/skills/monitoring-observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Monitoring Observability 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 Monitoring Observability 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 Monitoring Observability 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.

Monitoring & Observability

OpenTelemetry Setup

typescript
import { NodeSDK } from "@opentelemetry/sdk-node";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-http";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { PgInstrumentation } from "@opentelemetry/instrumentation-pg";
import { PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";

const sdk = new NodeSDK({
  serviceName: "order-service",
  traceExporter: new OTLPTraceExporter({
    url: "http://otel-collector:4318/v1/traces",
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter({
      url: "http://otel-collector:4318/v1/metrics",
    }),
    exportIntervalMillis: 15000,
  }),
  instrumentations: [
    new HttpInstrumentation(),
    new PgInstrumentation(),
  ],
});

sdk.start();
process.on("SIGTERM", () => sdk.shutdown());

Custom Spans and Metrics

typescript
import { trace, metrics, SpanStatusCode } from "@opentelemetry/api";

const tracer = trace.getTracer("order-service");
const meter = metrics.getMeter("order-service");

const orderCounter = meter.createCounter("orders.created", {
  description: "Number of orders created",
});

const orderDuration = meter.createHistogram("orders.processing_duration_ms", {
  description: "Order processing duration in milliseconds",
  unit: "ms",
});

async function createOrder(input: CreateOrderInput) {
  return tracer.startActiveSpan("createOrder", async (span) => {
    try {
      span.setAttributes({
        "order.customer_id": input.customerId,
        "order.item_count": input.items.length,
      });

      const start = performance.now();
      const order = await db.order.create({ data: input });

      orderCounter.add(1, { status: "success" });
      orderDuration.record(performance.now() - start);

      span.setStatus({ code: SpanStatusCode.OK });
      return order;
    } catch (error) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
      orderCounter.add(1, { status: "error" });
      throw error;
    } finally {
      span.end();
    }
  });
}

Prometheus Metrics

yaml
# prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: "api-servers"
    static_configs:
      - targets: ["api-1:9090", "api-2:9090"]
    metrics_path: /metrics

  - job_name: "node-exporter"
    static_configs:
      - targets: ["node-exporter:9100"]
typescript
import { collectDefaultMetrics, Counter, Histogram, Registry } from "prom-client";

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

const httpRequestDuration = new Histogram({
  name: "http_request_duration_seconds",
  help: "HTTP request duration in seconds",
  labelNames: ["method", "route", "status"],
  buckets: [0.01, 0.05, 0.1, 0.5, 1, 5],
  registers: [registry],
});

app.use((req, res, next) => {
  const end = httpRequestDuration.startTimer();
  res.on("finish", () => {
    end({ method: req.method, route: req.route?.path ?? req.path, status: res.statusCode });
  });
  next();
});

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

Structured Logging

typescript
import pino from "pino";

const logger = pino({
  level: process.env.LOG_LEVEL ?? "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
  redact: ["req.headers.authorization", "password", "token"],
});

function requestLogger(req, res, next) {
  const start = Date.now();
  res.on("finish", () => {
    logger.info({
      method: req.method,
      url: req.url,
      status: res.statusCode,
      duration_ms: Date.now() - start,
      trace_id: req.headers["x-trace-id"],
    });
  });
  next();
}

Alerting Rules

yaml
groups:
  - name: api-alerts
    rules:
      - alert: HighErrorRate
        expr: rate(http_request_duration_seconds_count{status=~"5.."}[5m]) / rate(http_request_duration_seconds_count[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 5% for {{ $labels.route }}"

      - alert: HighLatency
        expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 2
        for: 10m
        labels:
          severity: warning

Anti-Patterns

  • Logging sensitive data (passwords, tokens, PII) without redaction
  • Using string interpolation in log messages instead of structured fields
  • Creating unbounded cardinality in metric labels (e.g., user IDs as labels)
  • Not correlating logs and traces with a shared trace ID
  • Alerting on symptoms (high CPU) without understanding root cause
  • Missing SLO definitions before building dashboards

Checklist

  • OpenTelemetry SDK initialized with auto-instrumentation for HTTP, DB, and messaging
  • Custom spans added for business-critical operations
  • Metrics use bounded label cardinality
  • Structured logging with JSON output and secret redaction
  • Trace context propagated across service boundaries
  • Alerting rules based on SLOs (error rate, latency percentiles)
  • Dashboards show RED metrics (Rate, Errors, Duration) per service
  • Log retention and rotation policies configured

Frequently asked questions

What does the Monitoring Observability AI skill do?

Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging

Why use Monitoring Observability on TypingMind?

Because you install it once and use it with any model. Monitoring Observability 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 Monitoring Observability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/awesome-claude-code-toolkit/tree/main/skills/monitoring-observability. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Monitoring Observability?

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 Monitoring Observability?

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

Is the Monitoring Observability AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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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