/observability-and-instrumentation

name: observability-and-instrumentation
description: Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.

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

| Signal | Answers | Cost profile | Example | |---|---|---|---| | **Structured log** | "What happened in this specific case?" | Per-event; grows with traffic | `payment_failed` with provider error code | | **Metric** | "How often / how fast, in aggregate?" | Fixed per series; cheap to query | p99 latency of provider calls | | **Trace** | "Where did time go across services?" | Per-request; usually sampled | One slow checkout, broken down by hop |

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:**

| Level | Meaning | On-call action | |---|---|---| | `error` | Invariant broken; someone may need to act | Investigate | | `warn` | Degraded but handled (retry succeeded, fallback used) | Watch for trends | | `info` | Significant business event (order placed, job finished) | None | | `debug` | Diagnostic detail | Off in production by default |

**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) => {
  req.id = req.headers['x-request-id'] ?? crypto.randomUUID();
  req.log = logger.child({ requestId: req.id });
  res.setHeader('x-request-id', req.id);
  next();
});

**When several entry points write to one log, name the entry point.** A correlation ID identifies a run; it does not say which code path started it. The same job reached by a scheduler, by a replay endpoint, and by a manual CLI run produces interchangeable lines in one sink, so attributing a line falls back to elimination — cross-reading the scheduler's history, the process table, a deploy log — and that argument holds only as long as those external records happen to still exist. Stamp the entry point where the run starts, next to the correlation ID, and propagate both the same way:

// One helper for every entry point: the run's own logger carries both fields.
// `entryPoint`, not `source` — ECS reserves `source.*` for network fields.
export const runLog = (entryPoint: 'scheduler' | 'replay_endpoint' | 'cli', runId: string) =>
  logger.child({ entryPoint, requestId: runId });

// scheduler tick        -> runLog('scheduler', crypto.randomUUID())
// POST /jobs/:id/replay -> runLog('replay_endpoint', req.id)
// CLI invocation        -> runLog('cli', process.env.RUN_ID ?? crypto.randomUUID())

Both fields have to cross the same boundaries as the correlation ID — queue metadata, HTTP headers — or a worker re-derives the entry point and guesses. A field that merely correlates with an entry point is a hint, not an attribution: anything that can invoke the job can reproduce it.

**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: **R**ate (requests/sec), **E**rrors (failure rate), **D**uration (latency histogram, not avera