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 williamzujkowski/standards --skill monitoring-observabilitygit clone --depth 1 https://github.com/williamzujkowski/standardsWrote 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/williamzujkowski/standards/monitoring-observability)<a href="https://agentmods.dev/skills/williamzujkowski/standards/monitoring-observability"><img src="https://agentmods.dev/badge/skills/williamzujkowski/standards/monitoring-observability/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/williamzujkowski/standards/monitoring-observability"><img src="https://agentmods.dev/badge/skills/williamzujkowski/standards/monitoring-observability.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 | $0.02584 |
| Opus 5.5 | $0.00006 | $0.01034 |
| Sonnet 5.5 | $0.00003 | $0.00517 |
| Haiku 4.5 | $0.00001 | $0.00258 |
Grade A, and why
monitoring-observability 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 yesterday.
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.
curl 'http://localhost:9090/api/v1/query?query=up' This is a copy
88% identical to graphql-api-design — 513 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 450 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitoring & Observability
Level 1: Quick Reference
Three Pillars of Observability
Metrics - Numerical measurements over time
- Counter (only increases): request_total, errors_total
- Gauge (can go up/down): cpu_usage, memory_bytes
- Histogram (distribution): request_duration_seconds
- Summary (quantiles): response_time_summary
Logs - Timestamped event records
- Structured (JSON):
{"level":"error","msg":"connection failed","user_id":123} - Unstructured (text):
2025-01-15 ERROR: Connection timeout - Log levels: DEBUG, INFO, WARN, ERROR, FATAL
Traces - Request flow through distributed systems
- Span: Single operation (HTTP request, DB query)
- Trace: Collection of spans showing full request path
- Context propagation: Trace ID passed between services
Golden Signals (Google SRE)
Latency - How long requests take
Traffic - How many requests (RPS, QPS)
Errors - Rate of failed requests
Saturation - How "full" your service is (CPU, memory, disk, network)
Essential Checklist
- SLIs defined: Key user-facing metrics (availability, latency)
- SLOs set: Service Level Objectives (99.9% availability)
- Error budgets: 0.1% downtime = 43 minutes/month
- Alerting configured: On-call rotation, escalation policies
- Dashboards created: Service overview, system health
- Log aggregation: Centralized logging with retention policies
- Distributed tracing: Request path visualization
- Runbooks written: Step-by-step incident response guides
Quick Commands
# Prometheus - Query metrics
curl 'http://localhost:9090/api/v1/query?query=up'
# Check alerting rules
promtool check rules alert-rules.yml
# Grafana - Create API key
curl -X POST http://admin:admin@localhost:3000/api/auth/keys \
-H "Content-Type: application/json" \
-d '{"name":"deploy-key","role":"Admin"}'
# Elasticsearch - Check cluster health
curl -X GET "localhost:9200/_cluster/health?pretty"
# Jaeger - Query traces
curl "http://localhost:16686/api/traces?service=frontend&limit=10"
What ships with it
8 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.
- yesterday First seen · 450 lines · 14 tokens per session scan A dfd473876fd6
monitoring-observability is a skill published in the GitHub repository williamzujkowski/standards (18 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 2,584 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to graphql-api-design, differing in 513 lines, and is treated as a copy.
Other skills, from other repositories
Observability & Monitoring
Structured logging, metrics, distributed tracing, and alerting strategies.
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.
golang-observability-opentelemetry
Instrumenting Go applications with OpenTelemetry for distributed tracing, Prometheus for metrics, and structured logging with slog.
frontmcp-observability
Use when adding tracing, structured logging, metrics, or monitoring to a FrontMCP server. Covers zero-config OpenTelemetry distributed tracing across all flows; the this.telemetry API for custom spans, events, and attributes in tools, plugins, agents, and skills; structured JSON logging with trace correlation and…
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-logging
Application monitoring, logging systems, and alerting.