Agent skill · data analytics · sickn33
observability-and-instrumentation
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.
Why this skill is useful
Provides specific patterns for structured logging, metrics, and tracing that enhance production observability and diagnostics.
What it needs
About 7k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
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: 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 paymentfailed 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/observability-and-instrumentation--d9b91d