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 christopherlouet/claude-base --skill ops-monitoringgit clone --depth 1 https://github.com/christopherlouet/claude-baseWrote 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/christopherlouet/claude-base/ops-monitoring)<a href="https://agentmods.dev/skills/christopherlouet/claude-base/ops-monitoring"><img src="https://agentmods.dev/badge/skills/christopherlouet/claude-base/ops-monitoring/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/christopherlouet/claude-base/ops-monitoring"><img src="https://agentmods.dev/badge/skills/christopherlouet/claude-base/ops-monitoring.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 | $0.00023 | $0.00894 |
| Opus 5.5 | $0.00009 | $0.00358 |
| Sonnet 5.5 | $0.00005 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
ops-monitoring scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring Instrumentation
3 Pillars of Observability
- Logs - Discrete events
- Metrics - Numerical measurements
- Traces - Request paths
Structured Logs (Node.js)
import pino from 'pino';
const logger = pino({
level: process.env.LOG_LEVEL || 'info',
base: { service: 'api', env: process.env.NODE_ENV },
});
logger.info({ userId: '123', action: 'login' }, 'User logged in');
logger.error({ err, requestId }, 'Request failed');
Prometheus Metrics
import { Counter, Histogram, Registry } from 'prom-client';
const httpRequests = new Counter({
name: 'http_requests_total',
help: 'Total HTTP requests',
labelNames: ['method', 'path', 'status'],
});
const httpDuration = new Histogram({
name: 'http_request_duration_seconds',
help: 'Request duration',
labelNames: ['method', 'path'],
buckets: [0.1, 0.5, 1, 2, 5],
});
OpenTelemetry Traces
import { trace } from '@opentelemetry/api';
const tracer = trace.getTracer('my-service');
async function processOrder(orderId: string) {
return tracer.startActiveSpan('processOrder', async (span) => {
span.setAttribute('orderId', orderId);
try {
// ... processing
} finally {
span.end();
}
});
}
Health Checks
app.get('/health', (req, res) => res.json({ status: 'ok' }));
app.get('/ready', async (req, res) => {
const dbOk = await db.query('SELECT 1');
res.status(dbOk ? 200: 503).json({ db: dbOk });
});
Deploying the observability stack
Once the code is instrumented, deploy the backing stack (Prometheus + Grafana + Loki + Alertmanager).
- Mode: Docker Compose for dev/staging, Kubernetes + Helm for production (or Victoria Metrics / managed).
- Prometheus:
prometheus.ymlscrape configs +alert.rules.yml. - Alertmanager:
alertmanager.ymlroutes + receivers (Slack / PagerDuty / email). - Grafana: provision datasources + dashboards.
- Loki + Promtail: log aggregation; add
node-exporter+cAdvisorfor system metrics. - Persistent storage for metrics data; never expose Prometheus/Alertmanager without auth in production; configure alerts before going to prod and test the stack in staging first.
What ships with it
1 file 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.
- 1mo ago Changed · -2 lines b3fd0249a838
- 1mo ago Changed · -6 lines 81bcec50c780
- 1mo ago Changed · -1 lines 74f1c974cd0b
- 1mo ago First seen · 107 lines · 23 tokens per session scan A e9800d333fc2
ops-monitoring is a skill published in the GitHub repository christopherlouet/claude-base (5 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 894 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
agent-django-fastapi-expert
Specialist subagent: Django ORM, Django REST Framework, FastAPI async patterns, and Pydantic v2 specialist. Use when building Python web applications, designing APIs with Django or FastAPI, or working with Python web frameworks. Trigger phrases: Django, FastAPI, DRF, Django REST Framework, Pydantic, ORM, queryset…
microservices-architecture
Microservices architecture patterns and best practices. Use when designing distributed systems, breaking down monoliths, or implementing service communication.
authentication-patterns
OAuth 2.0, JWT, SSO, MFA, NextAuth/Clerk/Supabase Auth implementation patterns.
email-systems
Transactional email (Resend, SendGrid, SES), templates (React Email, MJML), deliverability (SPF/DKIM/DMARC), and inboxing best practices. Use when building email infrastructure, designing templates, or troubleshooting deliverability.
event-driven-architecture
Kafka, RabbitMQ, SQS/SNS, event sourcing, CQRS, saga patterns, dead letter queues, and idempotency. Use when designing asynchronous systems, implementing message-driven workflows, or building event streaming pipelines.
payment-integration
Stripe, RevenueCat, subscription billing, webhooks, PCI compliance, and tax. Use when integrating payments or subscriptions, handling payment webhooks, or reviewing billing and PCI scope.