observability-and-instrumentation

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• SKILL.md

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# 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:

```typescript
// 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:

```typescript
// 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();
});
```

**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 average). For resources (queues, pools, hosts), use **USE**: **U**tilization, **S**aturation, **E**rrors.

As with tracing, the vendor-neutral path is the OpenTelemetry metrics API (same SDK and context as step 5). The example below uses Prometheus' `prom-client` — one common backend choice, not the only one; the RED/USE and cardinality rules are identical either way.

```typescript
import { Histogram } from 'prom-client';

const httpDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'HTTP request duration',
  labelNames: ['method', 'route', 'status_class'],  // '2xx', not '200'
  buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
});
```

**Cardinality is the failure mode.** Every unique label combination is a separate time series. Labels must come from small, fixed sets (route template, status class, provider name). Never use user IDs, raw URLs, error messages, or other unbounded values as labels — that belongs in logs and traces.

```
OK as label:    route="/api/tasks/:id"   status_class="5xx"   provider="stripe"
NEVER a label:  user_id, email, request_id, full URL, error message text
```

Track averages never, percentiles always: an average hides the 1% of users having a terrible time. Use histograms and read p50/p95/p99.

### 5. Distributed tracing

Use OpenTelemetry — it's the vendor-neutral standard, and auto-instrumentation covers HTTP, gRPC, and common DB clients with near-zero code:

```typescript
// tracing.ts — must be imported before anything else

From 503496348-ops/mischief-production, MIT.

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