Observability and Instrumentation

Overview

Code you can't observe is code you can't operate. Observability is the ability to answer "what is the system doing and why?" from the outside, using the telemetry the code emits. Instrumentation is not a post-launch add-on — it's written alongside the feature, the same way tests are. If a feature ships without telemetry, the first user-reported bug becomes archaeology instead of a query.

When to Use

• Building any feature that will run in production
• Adding a new service, endpoint, background job, or external integration
• A production incident took too long to diagnose ("we couldn't tell what happened")
• Setting up or reviewing alerting rules
• Reviewing a PR that adds I/O, retries, queues, or cross-service calls

NOT for:

• Diagnosing a failure happening right now — use the debugging-and-error-recovery skill (observability is what makes that skill fast next time)
• Profiling and optimizing measured slowness — use the performance-optimization skill
• Launch-day monitoring checklists and rollback triggers — see the shipping-and-launch skill; this skill covers the instrumentation that feeds them

Process

1. Define "working" before instrumenting

Telemetry without a question is noise. Before adding any instrumentation, write down 2–4 questions an on-call engineer will ask about this feature:

FEATURE: checkout payment retry
QUESTIONS ON-CALL WILL ASK:
1. What fraction of payments succeed on first attempt vs after retry?
2. When a payment fails permanently, why? (provider error? timeout? validation?)
3. Is the payment provider slower than usual?
→ Every signal below must help answer one of these.

If you can't name the questions, you're not ready to instrument — you'll log everything and learn nothing.

2. Pick the right signal for each question

Rule of thumb: metrics tell you that something is wrong, traces tell you where, logs tell you why.

3. Structured logging

Log events, not prose. Every log line is a JSON object with a stable event name and machine-readable fields:

// BAD: string interpolation — unqueryable, inconsistent
logger.info(`Payment ${id} failed for user ${userId} after ${n} retries`);

// GOOD: stable event name + structured fields
logger.warn({
  event: 'payment_failed',
  paymentId: id,
  provider: 'stripe',
  errorCode: err.code,
  attempt: n,
}, 'payment failed');

Log levels — use them consistently:

Correlation IDs are mandatory. Generate (or accept) a request ID at the system boundary and attach it to every log line, span, and outbound call. Without it, you cannot reconstruct a single request from interleaved logs:

// Express: child logger per request, ID propagated downstream
app.use((req, res, next) => {
  const suppliedId = req.headers['x-request-id'];
  req.id = typeof suppliedId === 'string' && /^[A-Za-z0-9_-]{1,64}$/.test(suppliedId)
    ? suppliedId
    : crypto.randomUUID();
  req.log = logger.child({ requestId: req.id });
  res.setHeader('x-request-id', req.id);
  next();
});

Never log secrets, tokens, passwords, or full PII. This is a hard rule from the security-and-hardening skill — telemetry pipelines are a classic data-leak path. Allowlist fields; don't log whole request bodies.

4. Metrics

For request-driven services, instrument RED on every endpoint and every external dependency: Rate (requests/sec), Errors (failure rate), Duration (latency histogram, not average). For resources (queues, pools, hosts), use USE: Utilization, Saturation, Errors.

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