Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adrielp/ai-engineering-harness --skill otel_instrumentationgit clone --depth 1 https://github.com/adrielp/ai-engineering-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adrielp/ai-engineering-harness/otel_instrumentation)<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/otel_instrumentation"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/otel_instrumentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adrielp/ai-engineering-harness/otel_instrumentation"><img src="https://agentmods.dev/badge/skills/adrielp/ai-engineering-harness/otel_instrumentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.04537 |
| Opus 5 | $0.00027 | $0.02269 |
| Sonnet 5 | $0.00011 | $0.00907 |
| Haiku 4.5 | $0.00005 | $0.00454 |
Grade A, and why
otel_instrumentation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf http://otel-collector:4317 || echo "Collector unreachable (gRPC)" How it starts
The opening of the file, as written. The whole thing — 569 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenTelemetry Application Instrumentation
You are an expert OpenTelemetry instrumentation engineer. Produce prescriptive, opinionated guidance — not tutorials. Every recommendation must be production-grade.
Entrypoint: Detect → Scope → Act
1. Detect Language/Framework
| File | Language | Check for frameworks |
|---|---|---|
package.json |
Node.js | express, fastify, nestjs, next |
go.mod |
Go | gin, echo, fiber, net/http |
requirements.txt / pyproject.toml |
Python | flask, django, fastapi |
pom.xml / build.gradle |
Java | spring-boot, quarkus, micronaut |
*.csproj |
.NET | Microsoft.AspNetCore |
Gemfile |
Ruby | rails, sinatra |
2. Determine Scope
| Situation | Action |
|---|---|
| No OTel deps | Full SDK setup (§7) + all signals |
| SDK present, no custom instrumentation | Add custom spans/metrics/logs (§3–5) |
| Partial instrumentation | Audit, fill gaps, fix anti-patterns |
| Broken setup | Diagnose via validation checklist (§8) |
1. Resource Attributes (CRITICAL)
Resource attributes identify your service. This is the single highest-impact configuration for observability.
Required
| Attribute | Source | Strategy |
|---|---|---|
service.name |
Package manifest or env var | OTEL_SERVICE_NAME. Never accept unknown_service. |
service.version |
Git | git describe --tags --always at build time. Never hardcode. |
deployment.environment.name |
Env var | NODE_ENV, RAILS_ENV, FLASK_ENV, ASPNETCORE_ENVIRONMENT |
service.instance.id |
Generated | UUID v4 at startup. Never use hostname (not unique in k8s). |
service.namespace |
Convention | Logical grouping: payments, auth, catalog |
Environment Variables
OTEL_SERVICE_NAME=order-service
OTEL_RESOURCE_ATTRIBUTES=service.namespace=commerce,deployment.environment.name=production
OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4317
OTEL_EXPORTER_OTLP_PROTOCOL=grpc
Kubernetes: Use Downward API
env:
- name: OTEL_SERVICE_NAME
value: "order-service"
- name: POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
- name: POD_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: NODE_NAME
valueFrom:
fieldRef:
fieldPath: spec.nodeName
- name: OTEL_RESOURCE_ATTRIBUTES
value: >-
k8s.pod.name=$(POD_NAME),
k8s.namespace.name=$(POD_NAMESPACE),
k8s.node.name=$(NODE_NAME),
service.namespace=commerce,
deployment.environment.name=production
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 569 lines · 54 tokens per session scan A 82720d521330
otel_instrumentation is a skill published in the GitHub repository adrielp/ai-engineering-harness (20 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 4,537 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…