ahtishamshahzad/agent_dev_flow/backend-observability
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Use to plan backend observability — structured PII-safe logging with request correlation, metrics (rate/errors/duration, queues, jobs), health checks, alerting on symptoms, and tracing when the topology earns it.
Install
npx vskill@latest install ahtishamshahzad/agent_dev_flow/backend-observabilitySKILL.md
Backend Observability
Purpose
Make the backend diagnosable and its failures visible: structured logs you can query, metrics that answer "is it healthy," alerts that fire on user-facing symptoms, and correlation from request to log line.
When to Use
- When establishing a backend foundation, before incidents — not after the first one.
- Not for client-side analytics or product metrics.
Inputs
- Deployment/runtime shape (where logs/metrics can go); async surfaces (
queues,scheduled-jobs). - Critical user flows (what must alert when broken).
Discovery Questions
- Where do logs and metrics land (platform-provided vs chosen stack — a vendor decision)?
- Which flows are business-critical enough to page on?
- What volume/cost constraints bound log verbosity?
Responsibilities
- Structured logging: JSON logs with level, timestamp, request/correlation ID (generated at ingress, propagated through services, jobs, and outbound calls), route, actor ID (not PII), duration, outcome. Levels used consistently; debug off in production by config.
- PII/secret safety: denylist-by-design — no tokens, passwords, emails-in-clear, payload dumps; redaction at the logger, not by discipline (
backend-securityaudit events coordinate here). - Metrics: RED per endpoint class (rate, error %, duration percentiles), plus queue depth/job failures (
queues), scheduled-job heartbeats (scheduled-jobs), external-call latency/error per provider (third-party-integrations), DB pool/query health (../../database/database-performance). - Health checks: liveness (process up) vs readiness (dependencies reachable) — separate endpoints, used by the deploy target (
backend-deployment). - Alerting on symptoms: error-rate and latency thresholds on critical flows, DLQ growth, missed schedule heartbeats — every alert actionable, with a route to logs via the correlation ID.
- Tracing: adopt distributed tracing when multiple services/queues make log-hopping painful — a justified addition, not a default.
Required Workflow
- Choose log/metric destinations (approval if new vendor/infra).
- Define the log schema + correlation-ID propagation (HTTP → jobs → outbound).
- Define the metric set per surface (endpoints, queues, schedules, providers, DB).
- Define liveness/readiness checks.
- Define alerts for critical symptoms, each with an owner and a runbook line.
- Verify redaction with a test (secret-shaped values never reach the sink).
Decision Rules
- Log events, not narration: one entry per request/job outcome beats step-by-step spam.
- Alert on user-visible symptoms (error rate, latency, backlog), not causes (CPU) — causes go on dashboards.
- Correlation ID crosses every async boundary or debugging stops at the queue.
- Sampling/retention are cost decisions — record them; don't discover them on the invoice.
Rules
- No PII/secrets in logs — enforced by redaction code, verified by test.
- Every alert has an owner and an action; unactionable alerts get deleted, not muted.
- Health checks are cheap and dependency-honest (readiness fails when the DB is gone).
Anti-Patterns
console.logprose scattered through services.- Logging request/response bodies wholesale.
- One
/healthreturning 200 unconditionally. - Alerts on every 5xx blip → pager fatigue → ignored pages.
- Request ID that dies at the queue boundary.
Validation Checklist
- [ ] Log schema + levels + correlation propagation defined.
- [ ] Redaction wired and tested.
- [ ] Metrics per surface (RED, queues, schedules, providers, DB).
- [ ] Liveness/readiness split and wired to deploys.
- [ ] Symptom alerts with owners/runbooks.
- [ ] Tracing decision recorded (adopted or explicitly deferred).
Definition of Done
A recorded observability design — structured correlated PII-safe logs, per-surface metrics, honest health checks, actionable symptom alerts, and an explicit tracing decision — wired into deploy and job surfaces.
Related Skills
backend-error-handling, backend-performance, backend-security, queues, scheduled-jobs, third-party-integrations, backend-deployment, ../../performance-review.
Related Knowledge
../../../knowledge/ (critical flows, cost constraints).
Related References
../../../references/backend/observability/ (schema/alert tables, when populated).
Context Loading Guidance
- Requires: runtime shape, critical-flow list, async surface inventory.
- Does not require: vendor documentation, dashboard cosmetics.
- May load:
backend-error-handling(log points),queues/scheduled-jobs(metrics). - Stop when: schema, metrics, checks, and alerts are recorded.
Token Efficiency Guidance
Three tables carry the design: log schema fields, metric list per surface, alert list (symptom → threshold → owner).
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryChecking.ai/skills/backend/backend-observability/SKILL.md
Popularity
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