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observability-cli-guides

Query metrics, traces, logs, and profiles from observability backends via their CLIs.

9|2|Updated Aug 16, 2026
Search Tags:#logs#cli#metrics#observability#grafana#distributed tracing#azure monitor
One-click install
npx skills add https://github.com/using-system/oddyssey --skill observability-cli-guides-using-system
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: observability-cli-guides
Source: https://github.com/using-system/oddyssey/tree/main/.apm/skills/observability-cli-guides
Command: npx skills add https://github.com/using-system/oddyssey --skill observability-cli-guides-using-system

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Observing a running system from the terminal requires knowing each observability backend's CLI, authentication flow, and query syntax, which differ across Grafana, Datadog, Dynatrace, Azure Monitor, and AWS CloudWatch. This Skill centralizes per-stack references and ready-made query scripts so an agent can authenticate, discover what a service emits, and query it without memorizing each platform. ## Core Features & Use Cases - Per-stack references: One reference file per backend covering CLI setup, discovery-then-query commands per signal, configuration display, and what stack_config persists. - Query scripts: Deterministic Python scripts (grafana-.py, azure-monitor-.py, cloudwatch-*.py) for discovery, metrics, logs, traces, and connection proofs, each with --json output and the exact CLI commands recorded. - Custom stack support: A documented contract (references/CONTRACT.md) and a checker script for authoring guides for backends the package does not ship. - Use Case: While observing a load-test run against Azure Container Apps, use the azure-monitor-discover.py script to list what each service emits, then query traces and logs by operation_Id to diagnose a failing request. ## Quick Start Ask the agent to observe the current run against your configured observability stack, for example by requesting the slowest requests and their traces from Azure Monitor for the last 30 minutes.

Frequently Asked Questions about observability-cli-guides

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query Azure Monitor logs and traces from the CLI?โ–ผ

Use the az CLI with KQL through the azure-monitor-*.py scripts: discover what a service emits, then query customMetrics, requests, dependencies, and traces by appId and window. The scripts handle authentication checks, temporality probing, and output the exact az commands run.

How to query Grafana metrics and traces from the terminal?โ–ผ

Use the gcx CLI as described in references/grafana.md, with the grafana-*.py scripts providing the discovery and per-signal query commands. The local oddyssey stack also ships a ready-made gcx context via the setup-local-stack skill.

Which observability backends does this skill support?โ–ผ

It covers Grafana (self-hosted and Cloud), Datadog via Pup CLI, Dynatrace via dtctl and DQL, Azure Monitor via az and KQL, AWS CloudWatch and X-Ray via aws, and a local Grafana-based stack. The full list with aliases is in references/builtin-stacks.md.

Can I add a custom observability backend not in the built-in list?โ–ผ

Yes. Create a directory under .odd/observability-stacks/<name>/ with a guide.md following references/CONTRACT.md plus query scripts, then validate it with scripts/check_stack_reference.py. Guides can also be linked from a URL or git repository instead of copied.

What happens if my backend is not supported by any reference?โ–ผ

The skill instructs the agent to say so explicitly and fall back to the backend's documented REST API over curl, still applying the same authenticate, discover, then query method.

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