Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add miles990/claude-software-skills --skill monitoring-logginggit clone --depth 1 https://github.com/miles990/claude-software-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/miles990/claude-software-skills/monitoring-logging)<a href="https://agentmods.dev/skills/miles990/claude-software-skills/monitoring-logging"><img src="https://agentmods.dev/badge/skills/miles990/claude-software-skills/monitoring-logging/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/miles990/claude-software-skills/monitoring-logging"><img src="https://agentmods.dev/badge/skills/miles990/claude-software-skills/monitoring-logging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.03136 |
| Opus 5 | $0.00007 | $0.01568 |
| Sonnet 5 | $0.00003 | $0.00627 |
| Haiku 4.5 | $0.00001 | $0.00314 |
Grade A, and why
monitoring-logging scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
await axios.post( How it starts
The opening of the file, as written. The whole thing — 508 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring & Logging
Overview
Application observability through logging, metrics collection, monitoring dashboards, and alerting systems.
Structured Logging
Pino Logger (Node.js)
import pino from 'pino';
// Base logger configuration
const logger = pino({
level: process.env.LOG_LEVEL || 'info',
formatters: {
level: (label) => ({ level: label }),
bindings: () => ({}), // Remove pid and hostname
},
timestamp: pino.stdTimeFunctions.isoTime,
redact: {
paths: ['password', 'token', 'authorization', '*.password', '*.token'],
censor: '[REDACTED]',
},
});
// Child logger with context
function createRequestLogger(req: Request) {
return logger.child({
requestId: req.headers['x-request-id'] || crypto.randomUUID(),
method: req.method,
path: req.path,
userAgent: req.headers['user-agent'],
userId: req.user?.id,
});
}
// Express middleware
app.use((req, res, next) => {
req.log = createRequestLogger(req);
const startTime = Date.now();
res.on('finish', () => {
const duration = Date.now() - startTime;
req.log.info({
statusCode: res.statusCode,
duration,
contentLength: res.get('content-length'),
}, 'request completed');
});
next();
});
// Usage in handlers
app.get('/api/users/:id', async (req, res) => {
req.log.info({ userId: req.params.id }, 'fetching user');
try {
const user = await getUser(req.params.id);
req.log.debug({ user: user.id }, 'user found');
res.json(user);
} catch (error) {
req.log.error({ error }, 'failed to fetch user');
res.status(500).json({ error: 'Internal error' });
}
});
Log Levels
// Log level guidelines
logger.trace('Detailed debugging info'); // 10 - Very verbose
logger.debug('Debugging information'); // 20 - Debug mode only
logger.info('Normal operation events'); // 30 - Default level
logger.warn('Warning conditions'); // 40 - Potential issues
logger.error('Error conditions'); // 50 - Errors that need attention
logger.fatal('System-critical errors'); // 60 - System failure
// Contextual logging
logger.info({ orderId, userId, amount }, 'order placed');
logger.error({ error: err.message, stack: err.stack }, 'payment failed');
logger.warn({ retryCount, maxRetries }, 'retry attempt');
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 508 lines · 14 tokens per session scan A 8ea40882b7b1
monitoring-logging is a skill published in the GitHub repository miles990/claude-software-skills (20 stars, last pushed 7mo ago), licensed MIT. It adds 14 tokens to every session and 3,136 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
monitoring-expert
Expert-level monitoring and observability with Prometheus, Grafana, logging, and alerting. Use when the user mentions observability, Prometheus, Grafana, logging, metrics, or alerting, or when the task involves The Three Pillars of Observability, Monitoring Fundamentals, Prometheus Configuration, or Alert Rules.
monitoring-observability
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse v4 LLM tracing (astype, scorecurrentspan, shouldexportspan, LangfuseMedia), and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
datadog
Full-stack observability with Datadog APM, logs, metrics, synthetics, and RUM. Use when implementing monitoring, tracing, alerting, or cost optimization for production systems.
monitoring-alerting-commerce
Track store health in real time with dashboards for checkout success rate, payment failures, cart errors, and custom SLO alerting.
prom-query
Prometheus Metrics Query & Alert Interpreter — query metrics, interpret timeseries, triage alerts.
k8s-monitoring-alerting
A Kubernetes diagnostic and repair method for Prometheus and Grafana monitoring problems, including alerts that do not trigger. Prometheus collects measurements, while Grafana displays them in dashboards.