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oma-observability

Route observability work into setup, investigation, alerting, tracing, tuning, and routing playbooks.

46|2|Updated Mar 23, 2025
Search Tags:#observability#opentelemetry#tail sampling#privacy compliance#incident forensics#collector topology#observability-as-code
One-click install
npx skills add https://github.com/gracefullight/krds --skill oma-observability-gracefullight
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: oma-observability
Source: https://github.com/gracefullight/krds/tree/main/.agents/skills/oma-observability
Command: npx skills add https://github.com/gracefullight/krds --skill oma-observability-gracefullight

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents slow, guess-based observability work by routing the right investigation, tuning, and incident-forensics steps across signals, layers, boundaries, and vendor categories—while enforcing guardrails for meta-observability, sampling, privacy, and audit readiness.

Core Features & Use Cases

  • Intent-based routing for observability: classifies requests (setup, migrate, investigate, alert, trace, tune, route) and selects the correct playbook across the observability taxonomy.
  • Production-grade incident forensics (6-dimension narrowing): localizes root cause across code, service, layer, host, region, and infra using coordinated MELT+P signals with explicit validation steps.
  • Transport + meta-observability tuning: designs collector topology, transport choices, tail sampling correctness (trace-complete routing), and verifies pipeline self-health (delivery ratio, clock drift, cardinality, retention).
  • Privacy and compliance guardrails: applies W3C Trace Context/Baggage rules, PII redaction, retention matrices, and WORM/audit integrity checks to reduce regulatory risk.
  • Observability-as-code for SLOs and alerts: provides a GitOps-oriented approach for authoring SLO burn-rate alerts and collector/dashboard configuration with validation and anti-pattern prevention.

Quick Start

Ask the AI to run an incident workflow with the exact symptom and scope: “Investigate 5xx spike in ap-northeast-2 for the checkout service and produce a root-cause hypothesis with cross-signal evidence and rollback recommendation.”

Frequently Asked Questions about oma-observability

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

FAQPage Schema
How do I investigate a 5xx spike and localize the root cause across multiple signals?▼

Incident forensics uses a 6-dimension localization method to narrow the root cause across code, service, layer, host, region, and infrastructure. It coordinates MELT+P signals, validates trace continuity, and outputs evidence-backed hypotheses with rollback recommendations.

What is the best way to configure OpenTelemetry tail sampling without breaking traces?▼

Tail sampling correctness requires ensuring trace-complete routing across your collector topology. The workflow validates trace continuity using W3C tracecontext and baggage rules, checking pipeline delivery ratios and clock drift before confirming the sampling configuration is safe.

How do I enforce privacy and PII redaction rules in a multi-tenant observability pipeline?▼

Privacy compliance guardrails apply PII redaction, retention matrices, and WORM/audit integrity checks to telemetry pipelines. These rules enforce W3C baggage constraints and validate data handling across multi-tenant boundaries to reduce regulatory risk.

Can I use observability-as-code to manage SLO burn-rate alerts and collector configurations?▼

Observability-as-code provides a GitOps-oriented approach for authoring SLO burn-rate alerts and collector configurations. It includes validation steps and anti-pattern prevention to ensure alerting rules and dashboard setups are deployed safely and consistently.

What should I check when my collector topology experiences data loss or high cardinality?▼

Meta-observability tuning verifies pipeline self-health by checking delivery ratios, clock drift, cardinality spikes, and retention limits. It validates these transport and pipeline conditions before drawing any conclusions about telemetry data loss or degradation.

Does incident forensics work for Kubernetes and multi-region telemetry environments?▼

Incident forensics applies directly to Kubernetes and multi-region or multi-tenant telemetry design. The workflow routes intent-based investigation playbooks across MELT+P signals, validating W3C trace continuity and pipeline delivery before producing cross-signal evidence.

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