OpenTelemetry Pipeline Metrics Spec
comet-ml/opik
Specifies how to instrument an opik-backend pipeline with per-stage OpenTelemetry metrics for throughput, latency, errors and queue delay by workspace.
Structured logging with Pino/Winston, OpenTelemetry tracing, metrics collection, Grafana dashboards, and alerting rules.
$ npx skills add vibeeval/vibecosystem --skill observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install vibeeval/vibecosystem observability --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .claude/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/vibeeval/vibecosystem/tree/main/skills/observabilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add vibeeval/vibecosystem --skill observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install vibeeval/vibecosystem observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/observability .agents/skills/observability && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .agents/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install vibeeval/vibecosystem observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/observability .cursor/skills/observability && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .cursor/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/vibeeval/vibecosystem.git --path skills/observability--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add vibeeval/vibecosystem --skill observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install vibeeval/vibecosystem observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/observability .gemini/skills/observability && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .gemini/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install vibeeval/vibecosystem observabilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add vibeeval/vibecosystem --skill observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/observability .github/skills/observability && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .github/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vibeeval/vibecosystem --skill observability -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install vibeeval/vibecosystem observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vibeeval/vibecosystem.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/observability .opencode/skills/observability && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "observability" agent skill from https://github.com/vibeeval/vibecosystem/tree/main/skills/observability into .opencode/skills/observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "observability", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
observabilityStructured 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.
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.
Read from SKILL.md and the folder at commit 3b763b1. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
api.datadoghq.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
DD_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from vibeeval/vibecosystem at commit 3b763b1, republished under its MIT licence (© vibeeval). 181 words, ~3,347 tokens.
.claude/skills/observability/SKILL.md (or your agent's skills folder).Three pillars of observability: logs, traces, and metrics. Each answers different questions.
Pino is the fastest Node.js logger. Always emit JSON; never plain strings.
// 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]'
}
})// 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')# 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()# 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()| Level | When to Use | Example |
|---|---|---|
trace | Detailed execution path (dev only) | Function entry/exit, loop iterations |
debug | Diagnostic info for debugging | SQL queries, cache hit/miss |
info | Normal operations | Request received, job started, user login |
warn | Unexpected but recoverable | Retry attempt, fallback used, slow query |
error | Errors requiring investigation | DB connection failed, 3rd party API error |
fatal | Process must exit | Config missing, port in use |
// 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)Trace a request across multiple services by propagating a unique ID.
// 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() })
}// 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())// 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()
}
})
}// 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]
})// 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())
})// 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
}// 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]))# 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"// 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 })
})// 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 })
})# 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
}# Docker logging with size-based rotation
services:
api:
logging:
driver: json-file
options:
max-size: "50m"
max-file: "5"
labels: "service,version"// 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
Just SKILL.md in skills/observability of vibeeval/vibecosystem.
Open the folder on GitHubat commit 3b763b1
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Observability this skillvibeeval/vibecosystem | 531 | — | ~3.3k | Automated safety check: Pass | MIT | |
| OpenTelemetry Pipeline Metrics Speccomet-ml/opik | 22k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Archestra Dev Observabilityarchestra-ai/archestra | 4.3k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Frontmcp Observabilityagentfront/frontmcp | 146 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Monitoring Observabilityahmedasmar/devops-claude-skills | 203 | — | ~3.9k | Automated safety check: Pass | None | |
| Monitoring ExpertJeffallan/claude-skills | 12k | — | ~1.6k | Automated safety check: Pass | MIT |
comet-ml/opik
Specifies how to instrument an opik-backend pipeline with per-stage OpenTelemetry metrics for throughput, latency, errors and queue delay by workspace.
archestra-ai/archestra
A skill your agent uses when changing Archestra tracing, metrics, OpenTelemetry, Tempo, Grafana, Prometheus, LLM/MCP spans, observability labels, or local observability setup.
agentfront/frontmcp
A skill your agent uses when adding tracing, structured logging, metrics, or monitoring to a FrontMCP server.
ahmedasmar/devops-claude-skills
Monitoring and observability strategy, implementation, and troubleshooting.
Jeffallan/claude-skills
Sets up application monitoring: structured logs, Prometheus metrics, OpenTelemetry tracing, Grafana dashboards, alert rules and load tests with k6 or Artillery.
grafana/skills
Build a unified telemetry pipeline with Grafana Alloy — one OpenTelemetry-compatible binary that collects metrics, logs, traces, and profiles and ships to Grafana Cloud / Prometheus / Loki / Tempo /…
vibeeval/vibecosystem
Framework for measuring and tracking agent response quality over time.
vibeeval/vibecosystem
Security-focused differential code review with blast radius analysis, risk-adaptive depth (DEEP/FOCUSED/SURGICAL), git history correlation, and structured finding format.
vibeeval/vibecosystem
A skill your agent uses when making any factual claim about the codebase — existence, absence, or behavior.
vibeeval/vibecosystem
Systematic false positive verification for security findings.
vibeeval/vibecosystem
n8n otomasyon workflow'lari. An agent skill from vibeeval/vibecosystem.
vibeeval/vibecosystem
A skill your agent uses when context compression is imminent, when resuming a session, or when preserving critical decisions across long tasks.
Works with
Categories
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.
Observability fits situations like: tasks that involve Observability; tasks that involve Monitoring and alerting.
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.
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.
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.
Going by SKILL.md and its folder, Observability needs credentials named DD_API_KEY. Our summary lists: Python 3; Node.js.
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.
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.
Observability is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
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.
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.
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.