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Opentelemetry

Community
mizchi
opentelemetry

Platform-agnostic OpenTelemetry reference — signal selection (traces/metrics/logs), span design, context propagation (W3C TraceContext), sampling strategies, and OTLP exporter config. Use before writing any OTel instrumentation to get design decisions right. Platform-specific skills (otel-node, cloudflare-workers-otel-utels) layer on top of this.

Overview

Publishermizchi
Repositoryskills
Skill nameopentelemetry
Stars
333
Forks
4
Bundled files
Instructions only
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 mizchi on GitHub. Read the source before you install it.

Installation

Install the Opentelemetry 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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/opentelemetry .claude/skills/opentelemetry
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opentelemetry 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 Opentelemetry 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 Opentelemetry 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.

OpenTelemetry — Core Patterns

Signal Selection

Pick the right signal before writing code:

SignalUse forCost
TracesRequest lifecycle, latency attribution, distributed causalityHigh (per-request)
MetricsAggregated counts, rates, histograms — dashboards and alertingLow (pre-aggregated)
LogsDiscrete events with context — errors, audit, debugMedium

Rule of thumb: metrics answer "how often / how fast", logs answer "what happened", traces answer "why". Don't use traces where metrics suffice.

Span Design

Naming convention

<verb> <noun>         →  "fetch user", "send email"
<provider>.<operation> →  "db.query", "http.get", "cache.set"

Never put variable data (IDs, values) in the span name — use attributes. A span name is a cardinality key in your trace index.

Attributes

typescript
span.setAttributes({
  "db.system": "sqlite",
  "db.operation": "select",
  "db.sql.table": "users",
  "user.id": userId,          // high-cardinality: OK as attribute, not in name
});

Follow OpenTelemetry Semantic Conventions for well-known attribute names (http.*, db.*, rpc.*, etc.) — backends and APMs key off these.

Events vs child spans

  • Event: instant point-in-time inside the current operation (span.addEvent("cache_miss"))
  • Child span: has its own duration, latency matters, useful in trace waterfall

Status and exceptions

typescript
span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
span.recordException(err);  // adds exception.type / exception.message / exception.stacktrace

Call both on error. recordException alone does not flip status to ERROR — the span appears successful in the UI.

Guarantee span.end()

typescript
// ✓ callback form guarantees end()
tracer.startActiveSpan("operation", (span) => {
  try {
    return doWork();
  } catch (e) {
    span.recordException(e as Error);
    span.setStatus({ code: SpanStatusCode.ERROR });
    throw e;
  } finally {
    span.end();
  }
});

A span that is never ended leaks in the processor queue and may never export.

Context Propagation

W3C TraceContext (traceparent / tracestate) is the standard. Inject on outgoing requests, extract on incoming:

typescript
import { propagation, context } from "@opentelemetry/api";

// Outgoing HTTP
const carrier: Record<string, string> = {};
propagation.inject(context.active(), carrier);
fetch(url, { headers: carrier });

// Incoming (server handler)
const ctx = propagation.extract(context.active(), request.headers);
tracer.startActiveSpan("handle request", { context: ctx }, (span) => {
  // span is now a child of the upstream trace
});

Most common bug: creating a span without extracting the incoming context → the trace waterfall breaks into disconnected root spans. Always extract before starting the root server span.

Sampling

StrategyWhen
AlwaysOnDev / low-traffic staging
TraceIdRatioBased(0.1)High-volume production — sample 10% of new traces
ParentBased(root: TraceIdRatioBased)Recommended default — respects upstream decision, samples new roots at ratio
Tail sampling (OTel Collector)Need 100% of errors regardless of head-sample decision

ParentBased prevents the failure mode where the upstream samples a trace but the downstream drops it (broken waterfall).

OTLP Exporter Config

typescript
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base";

const exporter = new OTLPTraceExporter({
  url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT + "/v1/traces",
  headers: { Authorization: `Bearer ${process.env.OTEL_API_KEY}` },
});

provider.addSpanProcessor(
  new BatchSpanProcessor(exporter, {
    maxQueueSize: 512,
    scheduledDelayMillis: 2000,
    exportTimeoutMillis: 10_000,
  })
);

Use BatchSpanProcessor in production — it is async and low-overhead. SimpleSpanProcessor blocks the event loop; use only for local debugging.

Common Pitfalls

  • provider.register() not called: SDK is configured but nothing is exported. Call before any instrumentation runs.
  • ESM + auto-instrumentation (Node.js): require-in-the-middle hooks do not fire for ESM static imports. See otel-node for the workaround.
  • Cloudflare Workers: no Node.js runtime, fetch-boundary instrumentation needed. See cloudflare-workers-otel-utels.
  • Span name includes dynamic data: explodes trace index cardinality. Move to attributes.
  • No W3C propagation on outbound calls: distributed trace breaks — downstream spans appear as orphaned roots.

Frequently asked questions

What does the Opentelemetry AI skill do?

Platform-agnostic OpenTelemetry reference — signal selection (traces/metrics/logs), span design, context propagation (W3C TraceContext), sampling strategies, and OTLP exporter config. Use before writing any OTel instrumentation to get design decisions right. Platform-specific skills (otel-node, cloudflare-workers-otel-utels) layer on top of this.

Why use Opentelemetry on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/opentelemetry. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Opentelemetry?

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 Opentelemetry?

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

Is the Opentelemetry AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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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