Agent skill

Observability

by vibeeval in vibeeval/vibecosystem

Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

MITAuto-check passedDevOps & Cloud

Install Observability

skills CLI
$ npx skills add vibeeval/vibecosystem --skill observability -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vibeeval/vibecosystem observability --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/observability .claude/skills/observability && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
observability
GitHub stars
531
Token cost
~3.3k tokens
SKILL.md length
181 words
Files
1
Skills in repo
144
Repo updated
First seen
Licence
MIT

At a glance

Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

  • Tasks that involve Observability
  • SKILL.md covers Structured Logging with Pino…, Structured Logging with Python…, Log Levels Usage Guide and Request Correlation IDs, plus 9 more sections
  • Reaches api.datadoghq.com; needs DD_API_KEY
  • Tasks that involve Monitoring and alerting

What it does

Observability is an agent skill from vibeeval/vibecosystem. Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Observability and Monitoring and alerting. It works with OpenTelemetry and Grafana. The repository describes itself as: AI software team for Claude Code - 138 agents, 295 skills, 73 hooks. Self-learning, multi-agent swarm, autonomous skill evolution. The licence is MIT.

When your agent uses it

  • Tasks that involve Observability
  • Tasks that involve Monitoring and alerting

Example prompts

  • “/observability”

Requirements

  • Python 3
  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript, python, yaml, json and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.datadoghq.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DD_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Observability loads about 3.3k tokens when it runs. Until then it costs about 34 tokens; SKILL.md has 181 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~34
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 181 words, ~3,347 tokens.

Download SKILL.mdSave it as .claude/skills/observability/SKILL.md (or your agent's skills folder).
name
observability
description
Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

Observability Patterns

Three pillars of observability: logs, traces, and metrics. Each answers different questions.

Structured Logging with Pino (Node.js)

Pino is the fastest Node.js logger. Always emit JSON; never plain strings.

typescript
// logger.ts
import pino from 'pino'

export const logger = pino({
  level: process.env.LOG_LEVEL ?? 'info',
  formatters: {
    level(label) {
      return { level: label }        // emit "level":"info" not numeric
    }
  },
  base: {
    service: process.env.SERVICE_NAME ?? 'api',
    version: process.env.APP_VERSION ?? 'unknown',
    env: process.env.NODE_ENV ?? 'development'
  },
  timestamp: pino.stdTimeFunctions.isoTime,
  redact: {
    paths: ['req.headers.authorization', 'body.password', '*.token'],
    censor: '[REDACTED]'
  }
})
typescript
// Usage examples
import { logger } from './logger'

// Child logger with request context
const reqLogger = logger.child({
  requestId: crypto.randomUUID(),
  userId: user.id,
  path: req.path
})

reqLogger.info('Processing payment')
reqLogger.warn({ amount, currency }, 'Payment above threshold')
reqLogger.error({ err }, 'Payment failed')

Structured Logging with Python (structlog)

python
# logging_config.py
import structlog
import logging

structlog.configure(
    processors=[
        structlog.contextvars.merge_contextvars,
        structlog.processors.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.StackInfoRenderer(),
        structlog.processors.JSONRenderer(),
    ],
    wrapper_class=structlog.make_filtering_bound_logger(logging.DEBUG),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory(),
)

log = structlog.get_logger()
python
# Usage
log.info("request.received", path="/api/users", method="GET")
log.warning("rate_limit.approaching", user_id=user.id, count=95, limit=100)
log.error("payment.failed", exc_info=True, order_id=order.id, amount=99.99)

# Bind context for duration of request
structlog.contextvars.bind_contextvars(request_id=request_id, user_id=user_id)
log.info("order.created")   # request_id and user_id included automatically
structlog.contextvars.clear_contextvars()

Log Levels Usage Guide

LevelWhen to UseExample
traceDetailed execution path (dev only)Function entry/exit, loop iterations
debugDiagnostic info for debuggingSQL queries, cache hit/miss
infoNormal operationsRequest received, job started, user login
warnUnexpected but recoverableRetry attempt, fallback used, slow query
errorErrors requiring investigationDB connection failed, 3rd party API error
fatalProcess must exitConfig missing, port in use
typescript
// Good log message guidelines
// ✅ Include who, what, why, and relevant IDs
logger.info({ userId, orderId, amount }, 'order.created')

// ❌ Vague message, no context
logger.info('Order done')

// ✅ Error includes the actual error object
logger.error({ err, orderId }, 'order.payment.failed')

// ❌ Error swallowed or only string
logger.error('Payment error: ' + err.message)

Request Correlation IDs

Trace a request across multiple services by propagating a unique ID.

typescript
// Express middleware: assign or forward correlation ID
import { randomUUID } from 'crypto'
import { AsyncLocalStorage } from 'async_hooks'

const requestContext = new AsyncLocalStorage<{ requestId: string; userId?: string }>()

export function correlationMiddleware(req: Request, res: Response, next: NextFunction) {
  const requestId = (req.headers['x-request-id'] as string) ?? randomUUID()

  res.setHeader('x-request-id', requestId)

  requestContext.run({ requestId }, () => {
    next()
  })
}

// Get context anywhere in call stack (no prop drilling)
export function getRequestId(): string {
  return requestContext.getStore()?.requestId ?? 'unknown'
}

// Logger auto-includes correlation ID
export function getLogger() {
  return logger.child({ requestId: getRequestId() })
}

OpenTelemetry Tracing

typescript
// tracing.ts - must be imported FIRST before other modules
import { NodeSDK } from '@opentelemetry/sdk-node'
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http'
import { HttpInstrumentation } from '@opentelemetry/instrumentation-http'
import { ExpressInstrumentation } from '@opentelemetry/instrumentation-express'
import { PgInstrumentation } from '@opentelemetry/instrumentation-pg'

const sdk = new NodeSDK({
  serviceName: process.env.SERVICE_NAME ?? 'api',
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? 'http://localhost:4318/v1/traces'
  }),
  instrumentations: [
    new HttpInstrumentation(),
    new ExpressInstrumentation(),
    new PgInstrumentation()
  ]
})

sdk.start()

process.on('SIGTERM', () => sdk.shutdown())
typescript
// Manual spans for business logic
import { trace, SpanStatusCode, context } from '@opentelemetry/api'

const tracer = trace.getTracer('payment-service')

async function processPayment(orderId: string, amount: number) {
  return tracer.startActiveSpan('payment.process', async (span) => {
    span.setAttributes({
      'order.id': orderId,
      'payment.amount': amount,
      'payment.currency': 'USD'
    })

    try {
      const result = await chargeCard(amount)
      span.setStatus({ code: SpanStatusCode.OK })
      return result
    } catch (error) {
      span.recordException(error as Error)
      span.setStatus({ code: SpanStatusCode.ERROR, message: (error as Error).message })
      throw error
    } finally {
      span.end()
    }
  })
}

Custom Metrics with Prometheus

typescript
// metrics.ts
import { Registry, Counter, Histogram, Gauge } from 'prom-client'

export const registry = new Registry()

// HTTP request counter
export const httpRequestTotal = new Counter({
  name: 'http_requests_total',
  help: 'Total number of HTTP requests',
  labelNames: ['method', 'route', 'status_code'],
  registers: [registry]
})

// Request duration histogram
export const httpRequestDuration = new Histogram({
  name: 'http_request_duration_seconds',
  help: 'HTTP request duration in seconds',
  labelNames: ['method', 'route', 'status_code'],
  buckets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
  registers: [registry]
})

// Active connections gauge
export const activeConnections = new Gauge({
  name: 'active_connections',
  help: 'Number of active WebSocket connections',
  registers: [registry]
})
typescript
// Metrics middleware
export function metricsMiddleware(req: Request, res: Response, next: NextFunction) {
  const start = Date.now()

  res.on('finish', () => {
    const duration = (Date.now() - start) / 1000
    const labels = {
      method: req.method,
      route: req.route?.path ?? req.path,
      status_code: String(res.statusCode)
    }
    httpRequestTotal.inc(labels)
    httpRequestDuration.observe(labels, duration)
  })

  next()
}

// Metrics endpoint (scrape target for Prometheus)
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType)
  res.send(await registry.metrics())
})

Error Tracking with Sentry

typescript
// sentry.ts
import * as Sentry from '@sentry/node'
import { nodeProfilingIntegration } from '@sentry/profiling-node'

Sentry.init({
  dsn: process.env.SENTRY_DSN,
  environment: process.env.NODE_ENV,
  release: process.env.APP_VERSION,
  integrations: [nodeProfilingIntegration()],
  tracesSampleRate: process.env.NODE_ENV === 'production' ? 0.1 : 1.0,
  profilesSampleRate: 0.1,
  beforeSend(event, hint) {
    // Strip PII from errors
    if (event.user) {
      delete event.user.email
      delete event.user.ip_address
    }
    return event
  }
})

// Capture with context
try {
  await processOrder(orderId)
} catch (error) {
  Sentry.withScope((scope) => {
    scope.setTag('order.id', orderId)
    scope.setLevel('error')
    Sentry.captureException(error)
  })
  throw error
}

Grafana Dashboard Templates

json
// dashboard panel: Request Rate (PromQL)
{
  "title": "Request Rate",
  "type": "timeseries",
  "targets": [{
    "expr": "sum(rate(http_requests_total[5m])) by (route)",
    "legendFormat": "{{route}}"
  }]
}
# PromQL expressions for common panels

# Request rate (req/s over 5 min window)
sum(rate(http_requests_total[5m])) by (route, method)

# Error rate (%)
sum(rate(http_requests_total{status_code=~"5.."}[5m]))
  / sum(rate(http_requests_total[5m])) * 100

# Latency percentiles
histogram_quantile(0.50, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, route))
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, route))
histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, route))

# Apdex score (satisfied < 0.3s, tolerated < 1.2s)
(
  sum(rate(http_request_duration_seconds_bucket{le="0.3"}[5m]))
  + sum(rate(http_request_duration_seconds_bucket{le="1.2"}[5m]))
) / 2 / sum(rate(http_request_duration_seconds_count[5m]))

Alert Rules (SLO-Based)

yaml
# prometheus/alerts.yml
groups:
  - name: slo.alerts
    rules:
      # Error budget burn rate (fast burn = page immediately)
      - alert: HighErrorRate
        expr: |
          (
            sum(rate(http_requests_total{status_code=~"5.."}[5m]))
            / sum(rate(http_requests_total[5m]))
          ) > 0.01
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Error rate above 1% SLO"
          description: "Error rate is {{ $value | humanizePercentage }}"

      # p99 latency SLO breach
      - alert: HighLatencyP99
        expr: |
          histogram_quantile(0.99,
            sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
          ) > 1.0
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "p99 latency above 1s SLO"

      # Service availability
      - alert: ServiceDown
        expr: up{job="api"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "API service is down"

Health Check Monitoring

typescript
// Composite health check endpoint
interface HealthStatus {
  status: 'healthy' | 'degraded' | 'unhealthy'
  checks: Record<string, { status: string; latencyMs?: number; error?: string }>
}

app.get('/health/detailed', async (req, res) => {
  const checks: HealthStatus['checks'] = {}

  // Database check
  const dbStart = Date.now()
  try {
    await db.execute('SELECT 1')
    checks.database = { status: 'ok', latencyMs: Date.now() - dbStart }
  } catch (err) {
    checks.database = { status: 'fail', error: (err as Error).message }
  }

  // Redis check
  const redisStart = Date.now()
  try {
    await redis.ping()
    checks.redis = { status: 'ok', latencyMs: Date.now() - redisStart }
  } catch (err) {
    checks.redis = { status: 'fail', error: (err as Error).message }
  }

  const allHealthy = Object.values(checks).every(c => c.status === 'ok')
  const anyFailing = Object.values(checks).some(c => c.status === 'fail')

  const overall: HealthStatus['status'] = allHealthy
    ? 'healthy'
    : anyFailing ? 'unhealthy' : 'degraded'

  res.status(allHealthy ? 200 : 503).json({ status: overall, checks })
})

Dynamic Log Level in Production

typescript
// Change log level without restart
import { logger } from './logger'

app.put('/admin/log-level', requireAdminAuth, (req, res) => {
  const { level } = req.body
  const validLevels = ['trace', 'debug', 'info', 'warn', 'error', 'fatal']

  if (!validLevels.includes(level)) {
    return res.status(400).json({ error: 'Invalid level' })
  }

  logger.level = level
  logger.info({ level }, 'Log level changed')
  res.json({ level })
})

Log Rotation and Retention

bash
# logrotate config: /etc/logrotate.d/app
/var/log/app/*.log {
  daily
  rotate 14          # keep 14 days
  compress
  delaycompress
  missingok
  notifempty
  postrotate
    kill -USR1 $(cat /var/run/app.pid) 2>/dev/null || true
  endscript
}
yaml
# Docker logging with size-based rotation
services:
  api:
    logging:
      driver: json-file
      options:
        max-size: "50m"
        max-file: "5"
        labels: "service,version"

APM Integration (Datadog-style without vendor lock-in)

typescript
// OpenTelemetry collector config: otel-collector.yml
# ships to multiple backends simultaneously
exporters:
  otlp/datadog:
    endpoint: https://api.datadoghq.com/v1/traces
    headers:
      dd-api-key: ${DD_API_KEY}
  prometheus:
    endpoint: 0.0.0.0:8889
  loki:
    endpoint: http://loki:3100/loki/api/v1/push

pipelines:
  traces:
    receivers: [otlp]
    processors: [batch, resourcedetection]
    exporters: [otlp/datadog]
  metrics:
    receivers: [otlp, prometheus]
    exporters: [prometheus]
  logs:
    receivers: [otlp]
    exporters: [loki]

Key principle: Correlate logs, traces, and metrics by the same requestId/traceId. Emit structured JSON from day one — retrofitting is painful. Set up alerts on SLO burn rate, not absolute thresholds.

© vibeeval, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/observability of vibeeval/vibecosystem.

Open the folder on GitHubat commit 3b763b1

Compare with similar skills

Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Observability compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Observability this skillvibeeval/vibecosystem531—~3.3kAutomated safety check: PassMIT
OpenTelemetry Pipeline Metrics Speccomet-ml/opik22k—~3.2kAutomated safety check: PassApache-2.0
Archestra Dev Observabilityarchestra-ai/archestra4.3k—~1.2kAutomated safety check: PassCustom licence
Frontmcp Observabilityagentfront/frontmcp146—~4.6kAutomated safety check: PassApache-2.0
Monitoring Observabilityahmedasmar/devops-claude-skills203—~3.9kAutomated safety check: PassNone
Monitoring ExpertJeffallan/claude-skills12k—~1.6kAutomated safety check: PassMIT

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Categories

Questions about Observability

What does Observability do?

Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules. Observability is an agent skill from vibeeval/vibecosystem. Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.

When should I use Observability?

Observability fits situations like: tasks that involve Observability; tasks that involve Monitoring and alerting.

How do I install Observability in Claude Code?

Run `npx skills add vibeeval/vibecosystem --skill observability -a claude-code`. Or copy the skill folder (skills/observability in vibeeval/vibecosystem) into .claude/skills/observability in your project. Claude Code loads it when a task matches its description.

How do I install Observability in Codex?

Run `npx skills add vibeeval/vibecosystem --skill observability -a codex`. Or copy the skill folder (skills/observability in vibeeval/vibecosystem) into .agents/skills/observability in your project. Codex loads it when a task matches its description.

Can I use Observability in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vibeeval/vibecosystem --skill observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/observability, .gemini/skills/observability, .github/skills/observability and .opencode/skills/observability in your project.

What does Observability need to run?

Going by SKILL.md and its folder, Observability needs credentials named DD_API_KEY. Our summary lists: Python 3; Node.js.

Does Observability access the network?

SKILL.md names 1 domain. In commands or code: api.datadoghq.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Observability safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Observability use?

Observability is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Observability use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Observability?

Skills that share tags, products or a category with Observability: OpenTelemetry Pipeline Metrics Spec (comet-ml/opik, 22k stars), Archestra Dev Observability (archestra-ai/archestra, 4.3k stars), Frontmcp Observability (agentfront/frontmcp, 146 stars) and Monitoring Observability (ahmedasmar/devops-claude-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Observability?

vibeeval (a GitHub user) maintains it in vibeeval/vibecosystem, which has 531 GitHub stars. The repository holds 144 skills in this directory. The repository was last updated on August 8, 2026.

Source: vibeeval/vibecosystem on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.