Agent skill
logging-observability
Structured logging, distributed tracing, and metrics for production applications. [What: OpenTelemetry setup, log level strategy, correlation IDs, SLI/SLO alerting thresholds, Grafana dashboard design, PagerDuty integration] [When: setting up production logging, adding observability to a service, debugging distributed systems, designing alerting, implementing traces/metrics/logs] [Keywords: logging, observability, OpenTelemetry, OTel, structured logs, distributed tracing, correlation ID, metrics, Grafana, Prometheus, PagerDuty, Winston, Pino, structlog, log levels, SLI, SLO, alerting] NOT for application performance profiling (use a profiler), load testing, or database query optimization.
Install this agent skill to your Project
npx add-skill https://github.com/curiositech/some_claude_skills/tree/main/.claude/skills/logging-observability
Metadata
Additional technical details for this skill
- tags
-
observability logging tracing metrics opentelemetry monitoring
- category
- Code Quality & Testing
- pairs with
-
[ { "skill": "api-architect", "reason": "API request tracing and correlation IDs" }, { "skill": "devops-automator", "reason": "Deploying collectors and dashboards" }, { "skill": "background-job-orchestrator", "reason": "Distributed job observability" } ]
SKILL.md
Logging & Observability
Structured logging, distributed tracing, and metrics for production systems. Covers the full observability stack from log formatting to alert routing.
When to Use
Activate on: "structured logging", "distributed tracing", "OpenTelemetry", "OTel", "correlation ID", "log levels", "Grafana dashboard", "alerting thresholds", "SLI SLO", "Prometheus metrics", "PagerDuty integration", "observability stack", "Winston setup", "Pino logger", "log aggregation", "Datadog", "Honeycomb"
NOT for: Performance profiling (CPU/memory flamegraphs) | Load testing | Database query optimization | Security auditing
Decision Tree: What to Log at Each Level
flowchart TD
E[Event Occurs] --> Q1{Does it represent\na system failure?}
Q1 -->|Yes| Q2{Is it recoverable\nwithout human?}
Q2 -->|No| FATAL[FATAL: Service cannot\ncontinue — trigger pager]
Q2 -->|Yes| ERROR[ERROR: Operation failed,\nwill retry or degrade]
Q1 -->|No| Q3{Is it unexpected\nbut not failing?}
Q3 -->|Yes| WARN[WARN: Unusual condition,\ncircuit breaker open,\ndeprecation used]
Q3 -->|No| Q4{Is it a meaningful\nbusiness event?}
Q4 -->|Yes| INFO[INFO: User action,\npayment processed,\nservice started]
Q4 -->|No| Q5{Needed to debug\na specific issue?}
Q5 -->|Yes| DEBUG[DEBUG: DB queries,\ncache hits/misses,\nfunction inputs]
Q5 -->|No| TRACE[TRACE: Fine-grained\nloop iterations,\nOTel spans]
Rule of thumb: Production runs INFO and above. DEBUG only enabled per-service via dynamic config, never always-on in prod.
Core Patterns
Structured Log Format (JSON)
Every log line must be parseable. String concatenation is not a log.
Node.js with Pino:
import pino from 'pino';
const logger = pino({
level: process.env.LOG_LEVEL ?? 'info',
base: {
service: 'payment-service',
version: process.env.SERVICE_VERSION,
env: process.env.NODE_ENV,
},
redact: {
paths: ['req.headers.authorization', 'body.password', 'body.cardNumber', '*.ssn'],
censor: '[REDACTED]',
},
});
// Good: structured fields
logger.info({ orderId, userId, amountCents }, 'Payment processed');
// Bad: string interpolation
logger.info(`Payment processed for user ${userId} order ${orderId}`);
Python with structlog:
import structlog
log = structlog.get_logger()
structlog.configure(
processors=[
structlog.contextvars.merge_contextvars,
structlog.stdlib.add_log_level,
structlog.stdlib.add_logger_name,
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.JSONRenderer(),
]
)
# Good: key-value pairs
log.info("payment_processed", order_id=order_id, user_id=user_id, amount_cents=amount)
Correlation IDs
Every request needs a trace ID that flows through all downstream calls. This is the minimum viable distributed tracing without OTel.
// Express middleware
import { randomUUID } from 'crypto';
import { AsyncLocalStorage } from 'async_hooks';
const requestContext = new AsyncLocalStorage<{ traceId: string; spanId: string }>();
export function correlationMiddleware(req, res, next) {
const traceId = req.headers['x-trace-id'] ?? randomUUID();
const spanId = randomUUID().slice(0, 8);
requestContext.run({ traceId, spanId }, () => {
res.setHeader('x-trace-id', traceId);
next();
});
}
// Logger that auto-includes context
export function getLogger(name: string) {
return {
info: (msg: string, fields?: object) => {
const ctx = requestContext.getStore();
logger.info({ ...ctx, ...fields, logger: name }, msg);
},
// ... error, warn, debug
};
}
OpenTelemetry Setup
See references/opentelemetry-setup.md for complete OTel collector config, SDK initialization per language, and span attribute conventions.
Minimal Node.js bootstrap:
// Must be first import in entrypoint
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
const sdk = new NodeSDK({
serviceName: 'payment-service',
traceExporter: new OTLPTraceExporter({
url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
}),
instrumentations: [getNodeAutoInstrumentations()],
});
sdk.start();
Distributed Trace Propagation
sequenceDiagram
participant C as Client
participant GW as API Gateway
participant SVC as Payment Service
participant DB as Database
C->>GW: POST /checkout<br/>(no trace header)
Note over GW: Generate trace-id: abc123<br/>span-id: 0001
GW->>SVC: POST /payment<br/>traceparent: 00-abc123-0001-01
Note over SVC: Inherit trace-id: abc123<br/>New span-id: 0002
SVC->>DB: INSERT payment<br/>traceparent: 00-abc123-0002-01
Note over DB: Inherit trace-id: abc123<br/>New span-id: 0003
DB-->>SVC: OK (span 0003 ends)
SVC-->>GW: 200 OK (span 0002 ends)
GW-->>C: 200 OK (span 0001 ends)<br/>x-trace-id: abc123
The W3C traceparent header format: 00-{traceId}-{spanId}-{flags}. Always propagate this header on every downstream HTTP call.
Reference Files
| File | Contents |
|---|---|
references/opentelemetry-setup.md |
OTel SDK init per language, collector YAML config, span attributes, context propagation |
references/alerting-patterns.md |
SLI/SLO definitions, alert routing, PagerDuty severity mapping, alert fatigue prevention |
Anti-Patterns (Shibboleths)
Anti-Pattern 1: Logging PII or Secrets in Production
Novice thinking: "I'll just log the full request body to debug this auth issue."
Why wrong: GDPR/CCPA violations carry fines up to 4% of global revenue. Secrets in logs propagate to log aggregators, S3 exports, audit trails — all places with different access controls. A single console.log(req.body) can expose thousands of user passwords in your Datadog dashboard.
Detection signature: Search your logs for password, ssn, cardNumber, authorization as field values (not keys). If any appear, you have a PII leak.
Fix — Allowlist approach:
// Never log what you don't explicitly approve
const SAFE_BODY_FIELDS = ['orderId', 'productId', 'quantity', 'currency'];
logger.info({
body: pick(req.body, SAFE_BODY_FIELDS), // only known-safe fields
path: req.path,
method: req.method,
}, 'Request received');
Fix — Redaction in logger config:
// Pino's redact runs before any transport
const logger = pino({
redact: {
paths: [
'req.headers.authorization',
'req.headers.cookie',
'body.password',
'body.*.password', // nested objects too
'body.cardNumber',
'body.ssn',
'*.token',
'*.secret',
],
censor: '[REDACTED]',
},
});
Shibboleth: An expert sets up redaction at logger initialization, not as a reminder comment. Redaction must be structural, not ad-hoc.
Anti-Pattern 2: Unstructured String Logs Instead of Structured JSON
Novice thinking: logger.info('User ' + userId + ' purchased ' + productId + ' for $' + amount)
Why wrong: You cannot filter, aggregate, or alert on string-interpolated data in any log aggregator. A Grafana query for amount > 1000 requires amount to be a numeric field, not embedded in a sentence. String logs are write-only — you can read them but not query them at scale.
Impact: Your 10 million daily log lines become unsearchable. MTTR (mean time to recovery) during incidents doubles because engineers grep through strings instead of filtering structured fields.
Fix:
// Bad: string log — amount is buried in text
logger.info(`User ${userId} purchased ${productId} for $${amount}`);
// Good: structured — every field is queryable
logger.info({ userId, productId, amountDollars: amount / 100 }, 'purchase_completed');
Consistent event naming: Use snake_case verb-noun event names (payment_processed, user_signed_up, order_failed) as the message string. This creates a stable vocabulary for dashboards and alerts.
Shibboleth: The log message string is for humans scanning log tails. All queryable data lives in structured fields. A logger that produces {} as its output shape is better than one that produces readable strings.
Anti-Pattern 3: Log-and-Throw (Duplicate Log Entries)
Novice thinking: Log the error, then re-throw so the caller also knows about it.
// BAD: log-and-throw
async function processPayment(orderId: string) {
try {
return await chargeCard(orderId);
} catch (err) {
logger.error({ err, orderId }, 'Payment failed'); // Logged here
throw err; // And the caller logs it again
}
}
async function handleCheckout(req, res) {
try {
await processPayment(req.body.orderId);
} catch (err) {
logger.error({ err }, 'Checkout failed'); // Same error logged twice
res.status(500).json({ error: 'Checkout failed' });
}
}
Why wrong: Every error appears 2-5 times in your logs depending on call depth. Alerting on error count becomes unreliable. Incident review is confusing — engineers think there were multiple failures. Log volume costs money (Datadog charges per ingested GB).
Fix — Log only at the boundary where you handle the error:
// Good: log only where you decide what to do with the error
async function processPayment(orderId: string) {
// No try-catch: let errors propagate naturally
return await chargeCard(orderId);
}
async function handleCheckout(req, res) {
try {
await processPayment(req.body.orderId);
res.json({ success: true });
} catch (err) {
// One log, at the boundary where we're deciding to return 500
logger.error({ err, orderId: req.body.orderId }, 'checkout_failed');
res.status(500).json({ error: 'Checkout failed' });
}
}
Rule: Log where you handle. Don't log where you propagate. The call stack in the error object already tells you where it originated.
Shibboleth: If you see the same traceId appear in more than two error log lines for a single request, you have a log-and-throw chain somewhere.
Quality Checklist
[ ] All log lines are JSON (no string concatenation)
[ ] Log level strategy documented: what goes at each level
[ ] PII/secrets redacted at logger config level, not call site
[ ] Correlation IDs propagated on all outbound HTTP calls
[ ] OTel SDK initialized before any other imports
[ ] Error logs include the error object (not just message)
[ ] No log-and-throw patterns in error handling
[ ] DEBUG logs use conditional guards or sampling
[ ] SLI/SLO defined for each critical user journey
[ ] Alert routing: notify vs page threshold documented
[ ] Runbook linked from every paging alert
[ ] Log retention policy set (cost vs compliance)
Output Artifacts
- Logger configuration — Pino/Winston/structlog setup with redaction rules
- OTel bootstrap file — SDK init with auto-instrumentation
- Correlation middleware — AsyncLocalStorage request context
- Prometheus metrics module — Counter/histogram/gauge definitions
- Grafana dashboard JSON — Four golden signals panels
- Alertmanager rules YAML — SLO-based alert definitions
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
3d-cv-labeling-2026
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
project-management-guru-adhd
Expert project manager for ADHD engineers managing multiple concurrent projects. Specializes in hyperfocus management, context-switching minimization, and parakeet-style gentle reminders. Activate on 'ADHD project management', 'context switching', 'hyperfocus', 'task prioritization', 'multiple projects', 'productivity for ADHD', 'task chunking', 'deadline management'. NOT for neurotypical project management, rigid waterfall processes, or general productivity advice without ADHD context.
large-scale-map-visualization
Master of high-performance web map implementations handling 5,000-100,000+ geographic data points. Specializes in Leaflet.js optimization, Supercluster algorithms, viewport-based loading, canvas rendering, and progressive disclosure UX patterns.
adhd-design-expert
Designs digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.
liaison
Translate multi-agent ecosystem activity into human-readable status briefings, decision requests, and progress summaries. Use for 'status update', 'brief me', 'what happened', 'summarize progress'. NOT for project planning (use project-management-guru-adhd), code review, or technical documentation.
windows-95-web-designer
Modern web applications with authentic Windows 95 aesthetic. Gradient title bars, Start menu paradigm, taskbar patterns, 3D beveled chrome. Extrapolates Win95 to AI chatbots, mobile UIs, responsive layouts. Activate on 'windows 95', 'win95', 'start menu', 'taskbar', 'retro desktop', '95 aesthetic', 'clippy'. NOT for Windows 3.1 (use windows-3-1-web-designer), vaporwave/synthwave, macOS, flat design.
Didn't find tool you were looking for?