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 SigNoz/agent-skills --skill signoz-setting-up-observabilitygit clone --depth 1 https://github.com/SigNoz/agent-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/signoz/agent-skills/signoz-setting-up-observability)<a href="https://agentmods.dev/skills/signoz/agent-skills/signoz-setting-up-observability"><img src="https://agentmods.dev/badge/skills/signoz/agent-skills/signoz-setting-up-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/signoz/agent-skills/signoz-setting-up-observability"><img src="https://agentmods.dev/badge/skills/signoz/agent-skills/signoz-setting-up-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00224 | $0.05951 |
| Opus 5.5 | $0.00090 | $0.02380 |
| Sonnet 5.5 | $0.00045 | $0.01190 |
| Haiku 4.5 | $0.00022 | $0.00595 |
Grade A, and why
signoz-setting-up-observability 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 — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setting Up Observability After Ingestion
Take a service from "telemetry is flowing into SigNoz" to "I have a
dashboard, alerts, saved views, and a tuning loop." This skill is an
orchestration layer, not a standalone reference: the mechanical
work lives in the sibling SigNoz skills and the MCP tools/resources hold
the current payload schemas. This skill sequences them and supplies the
judgment calls (scope, SLOs, thresholds, what to skip) that no single
skill owns; it deliberately does not restate payload schemas or field
rules, so read the relevant signoz://… resource before composing any
payload.
Use this when traces, logs, or metrics are already landing in SigNoz and you want an opinionated, SLO-aware setup, not when you're still wiring up the SDK. Audience: two consumers, an autonomous AI SRE agent that runs without a human in the loop (e.g. an in-product SigNoz assistant), and an engineer at a Claude Code / Codex / Cursor prompt. Both follow the same flow; where a step needs a human decision (Phase 1 triage, Phase 5 sign-off), the host decides how to surface it, and an autonomous host fills the gap from upstream context instead of blocking.
Prerequisites
This skill calls SigNoz MCP server tools (signoz_list_services,
signoz_list_dashboards, signoz_get_field_keys,
signoz_execute_builder_query, signoz_create_dashboard,
signoz_create_alert, signoz_create_view, etc.) and
delegates to sibling skills. Before running the workflow, confirm the
signoz_* tools are available. If they are not, the SigNoz MCP
server is not installed or configured; run signoz-mcp-setup first. Do
not fall back to raw HTTP calls or fabricate payloads.
When to use
Use this skill when the user wants the whole post-ingestion setup: SLI/SLO → exploration → dashboard → views → alerts → tuning, in any combination of those deliverables, sequenced as one workflow.
Do NOT use this skill when the user wants a single artifact; hand off directly:
- Just a dashboard →
signoz-creating-dashboards. - Just one static / threshold alert or notification rule →
signoz-creating-alerts. (But SLO / burn-rate / error-budget alerting, even with no dashboard, stays here: it needs the SLI/SLO capture and burn-rate judgment in the phases below.) - Just a saved Explorer view →
signoz-managing-views. - A one-off exploratory query →
signoz-generating-queries. - A concept/doc lookup (SRE methods, OTel conventions, burn-rate math)
→
signoz-searching-docs/signoz_search_docs.
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 4fe5c6c3a207
- 1mo ago Changed 864c9f91a464
- 1mo ago First seen · 456 lines · 224 tokens per session scan A 7761c62bb4be
signoz-setting-up-observability is a skill published in the GitHub repository SigNoz/agent-skills (19 stars, last pushed yesterday), licensed MIT. It adds 224 tokens to every session and 5,951 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
shipping-and-launch
Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
deploy-slack-app
Use when a developer wants to deploy, host, or run a Slack app somewhere it keeps working after the local process stops, or mentions Railway, Heroku, railway up, git push heroku, slack deploy, a deploy hook in .slack/hooks.json, hosting a Socket Mode or Request URL (HTTP) Bolt app, deploying from Windows, graduating…
otel-collector-builder
Build custom OpenTelemetry Collector distributions with OCB (OpenTelemetry Collector Builder). Use when authoring or debugging a builder manifest (builder.yaml), choosing component and provider versions, building a collector that bundles a custom or out-of-distribution component, setting up CI or Docker builds of a…
otel-profiles
OpenTelemetry profiles signal and the eBPF profiler (otelcol-ebpf-profiler, profiling receiver). Use when setting up continuous profiling with OpenTelemetry, deploying the eBPF profiler on Linux hosts, Docker, or Kubernetes (DaemonSet, kind, k3d), building a profiles pipeline, sending OTLP profiles to Pyroscope…
huawei-cloud-agentarts-otel-ingestion
OpenTelemetry ingestion skill for Huawei Cloud AgentArts observability platform. Provides OTel telemetry data (trace/metrics/logs) ingestion into AgentArts agent-ops, AI Agent observation with genai semantic conventions, auto-configuration for Java/Python/Node.js/Go applications, and ingestion diagnostics and…
setting-up-wide-events-store
Use when a human asks to set up, deploy, or finish deploying the bundled Wide Events DuckDB store (the Kamal accessory that receives production telemetry). Covers the store generator, DNS, kamal setup, and post-deploy verification end to end.