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 JayRHa/AgentSkills --skill observability-setupgit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/observability-setup)<a href="https://agentmods.dev/skills/jayrha/agentskills/observability-setup"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/observability-setup/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/jayrha/agentskills/observability-setup"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/observability-setup.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 | $0.00127 | $0.01888 |
| Opus 5.5 | $0.00051 | $0.00755 |
| Sonnet 5.5 | $0.00025 | $0.00378 |
| Haiku 4.5 | $0.00013 | $0.00189 |
Grade A, and why
observability-setup 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Setup
Overview
Observability is the ability to ask arbitrary questions about a system's behavior from the outside, using its telemetry. This skill instruments a service across the three pillars — structured logs, metrics, and distributed traces — then turns raw signals into SLIs, SLOs, error budgets, and actionable alerts. The goal is not "more dashboards" but the ability to answer "is the user in pain, and where?" in under five minutes.
Keywords: observability, OpenTelemetry, OTel, structured logging, JSON logs, Prometheus, metrics, RED method, USE method, distributed tracing, spans, trace context, correlation ID, SLI, SLO, error budget, burn rate, alerting, Alertmanager, Grafana, on-call, golden signals, cardinality, exemplars.
Standardize on OpenTelemetry (OTel) for instrumentation — it is vendor-neutral and emits to Prometheus, Tempo, Loki, Datadog, Honeycomb, etc. Keep instrumentation code free of vendor SDKs; push vendor choice to the OTel Collector.
Workflow
- Inventory the service. Identify language/runtime, request entry points (HTTP/gRPC/queue), critical user journeys, downstream dependencies, and the existing telemetry backend (or pick one). Read
references/instrumentation-patterns.mdfor per-pillar guidance. - Add structured logging first. Emit one JSON event per request with a stable schema (timestamp, level, message,
trace_id,span_id, service, plus event-specific fields). Never log secrets/PII. Logs become useful only once they are queryable and correlated to traces. - Instrument metrics using RED + USE. For request-driven services apply RED (Rate, Errors, Duration); for resources apply USE (Utilization, Saturation, Errors). Use the four golden signals (latency, traffic, errors, saturation) as the checklist. Watch label cardinality.
- Add distributed tracing. Auto-instrument the framework, propagate W3C
traceparentacross service boundaries, and add manual spans around meaningful business operations. Attachtrace_idto every log line and metric exemplar so you can pivot signal → signal. - Wire the OTel Collector. Run a collector (agent + gateway) to receive OTLP, batch, sample, and export. This decouples app code from backends and centralizes sampling/redaction. See
references/instrumentation-patterns.md. - Define SLIs and SLOs. Pick 1–3 SLIs per user journey (availability, latency, correctness). Set realistic SLO targets and a measurement window. Use
references/slo-cookbook.mdand the template intemplates/slo-spec.md. - Compute error budgets and burn-rate alerts. Alert on budget burn rate, not on raw thresholds. Use multi-window multi-burn-rate alerts to balance fast detection against false pages. Generate rules with
scripts/gen_slo_alerts.py. - Validate and reduce noise. Run
scripts/check_slo_burn.pyagainst live metrics to sanity-check. Ensure every alert is actionable, links to a runbook, and pages a human only when users are affected. Everything else is a ticket or a dashboard.
What ships with it
6 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.
- 1mo ago First seen · 81 lines · 127 tokens per session scan A 47ac85b76a48
observability-setup is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 127 tokens to every session and 1,888 once invoked, about $0.0005 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 2026-08-31.
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