Skill observability

Skill saitarrun/sdlc-ai-workflow/skills/skill-observability

A comprehensive Claude Code plugin that automates the complete Software Development Lifecycle with 20 role-specific agents, 12 knowledge skills, and 8 commands, all guided by principles.

Install

npx -y skills add saitarrun/sdlc-ai-workflow --skill skill-observability

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

Metrics, logging, distributed tracing, dashboards, alerting, SLO-driven observability, post-incident analysis. Use when designing monitoring, setting up dashboards, creating alert rules, or analyzing production data.

SKILL.md

2.4 KB, 661 tokens by cl100k_base, as published. Nobody here has run it

Skill: Observability (Metrics, Logs, Traces)

Three pillars: Metrics (what), Logs (why), Traces (how). Use all three.

Metrics (Time-Series Data)

Key metrics:

Request latency (p50, p95, p99)
Error rate (%)
Throughput (req/s)
Resource (CPU, memory, disk)

Collection: Prometheus, StatsD, CloudWatch

api_latency_seconds{service="auth", endpoint="/login"} = 0.042
error_rate{service="api"} = 0.001
db_connections{pool="default"} = 42

Logging (Structured)

Format: JSON for parsing

{
  "timestamp": "2024-01-15T10:30:45Z",
  "level": "ERROR",
  "service": "user-service",
  "message": "Failed to create user",
  "error": "database_error",
  "user_id": "user-123",
  "duration_ms": 150
}

Sampling: Log 100% errors, 1% normal (reduce volume)

Distributed Tracing

Trace request end-to-end:

Request: POST /api/users
├─ Auth span (5ms)
├─ Validate span (2ms)
├─ DB insert span (45ms)
│  ├─ Connection span (1ms)
│  └─ Query span (44ms)
└─ Cache update span (3ms)
Total: 55ms

Tools: Jaeger, Zipkin, Datadog

Dashboards (SLO-Focused)

What to display:

SLO % (100% target, 99% actual)
Error budget consumed (%)
Latency (p95, p99)
Errors per minute
Active requests
Alert status

Tools: Grafana, CloudWatch, Datadog

Alerting

Good alert (actionable):

Alert: DB connection pool > 80%
Severity: CRITICAL
Action: Increase pool size or check for connection leaks
Runbook: https://wiki.example.com/alerts/db-pool-high

Bad alert (not actionable):

Alert: Disk usage > 50%
(What should I do?)

SLO-Driven Alerting

SLO: 99.9% uptime
Error budget: 8.64 hours/month

Alert: If burn rate > 1% → 30 days to fail budget
Alert: If burn rate > 10% → 3 days to fail budget

Monitoring Checklist

Status: Ready for observability work
Best for: Metrics, logging, alerting, SLO monitoring

Gives 2 of the 12 instructions most monitoring observability skills give in 661 tokens

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

link every alert to a runbookhere, and in 43 of 481, across 35 files
use structured json logginghere, and in 36 of 481, across 31 files
alert on user-facing symptomsin 20 of 481, across 15 files
emit structured JSON logs with stable event namesin 18 of 481, across 13 files
propagate trace context across boundariesin 16 of 481
use histograms for latency trackingin 14 of 481, across 9 files
use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
include a correlation ID on every log linein 13 of 481, across 8 files
Define service level objectivesin 10 of 481, across 7 files
Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
define on-call questions before adding telemetryin 9 of 481, across 4 files

Said here and by no other author read

collect request latency, error rate, throughput, and resource metrics
sample one percent of normal traffic
display SLO percentage and error budget consumed

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.