observability-and-instrumentation

Guides adding logs, metrics, traces, alerts, SLOs, and audit events to backend systems.

1|Updated Sep 10, 2026
One-click install
npx skills add https://github.com/thienty1207/Hotel_Staff --skill observability-and-instrumentation-thienty1207
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: observability-and-instrumentation
Source: https://github.com/thienty1207/Hotel_Staff/tree/main/.baron/core/skills/observability-and-instrumentation
Command: npx skills add https://github.com/thienty1207/Hotel_Staff --skill observability-and-instrumentation-thienty1207

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Production systems often lack the signals needed to diagnose failures, forcing engineers to guess why requests failed, which dependency timed out, or who performed a sensitive action. This Skill provides structured guidance for adding verifiable operational visibility without leaking secrets or PII. ## Core Features & Use Cases - Signal Design: Plans structured logs, low-cardinality metrics, distributed traces, correlation id propagation, SLOs, actionable alerts, and append-only audit events. - Privacy Guardrails: Enforces redaction of secrets, tokens, session cookies, payment data, and PII across logs, spans, labels, and audit records. - Verification Discipline: Requires tests, smoke checks, or captured evidence before claiming a system is observable, and marks unverified production behavior as unknown. - Use Case: When adding a payment webhook handler to a Go API, use this Skill to decide which spans, counters, and audit events to emit, how to propagate the correlation id, and how to verify redaction before shipping. ## Quick Start Ask the AI to review this backend endpoint and propose an observability plan covering logs, metrics, traces, and audit events with PII-safe fields.

Frequently Asked Questions about observability-and-instrumentation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add structured logging to a backend API?

Emit structured fields instead of plain strings, include a correlation or request id, and use consistent levels: debug for local detail, info for lifecycle events, warn for recoverable risk, error for failures. Never log secrets, tokens, or full request bodies without explicit redaction.

What metrics should I track for an HTTP service?

Track low-cardinality, aggregatable signals: counters for requests, failures, and retries; histograms for latency and queue wait; gauges for active workers or open connections. Avoid labels like user email, raw URLs, or unbounded error text that break aggregation.

How do I propagate a correlation id across services?

Generate or accept the id at the trust boundary, then pass it through logs, trace spans, job payloads, and outbound provider calls when safe. Never use it for authentication, authorization, or tenant proof, and do not trust arbitrary client-supplied ids for security decisions.

Can I log user data for debugging production issues?

Treat PII as sensitive by default and prefer stable opaque IDs over emails, names, or phone numbers. Redact before writing logs, traces, metrics, or audit records; if redaction behavior is unknown, mark it unknown rather than logging raw data.

When should I not add more instrumentation?

Skip instrumentation when no operator question exists for the signal, such as pure UI copy changes or local-only refactors with no runtime impact. Prefer a few useful, verifiable signals over noisy volume that future maintainers cannot interpret.