agentmods.dev

oma-observability

oma-observability is a skill for Claude Code, Codex from gracefullight/docusaurus-plugins. It costs 78 tokens per session (4,437 once invoked), scanned A, a copy of oma-observability, MIT.

An observability specialist for collecting and connecting software signals such as metrics, logs, traces, profiles, and user-activity data. Observability means gathering evidence that helps explain what software is doing and why it fails.

In plain words
What is it for?
Use it to design OpenTelemetry pipelines, connect services across boundaries, tune collectors and transport, define service objectives, and perform incident investigations.
Why use it?
It helps trace problems across services and infrastructure, tune how telemetry is transported, and investigate incidents by locating the affected layer or system.

Skill for Claude CodeCodex

not rated 22 2mo ago A scan 78 tokens copy · 89% MIT
View the source file gracefullight/docusaurus-plugins
Install

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.

agentmods
npx agentmods add skills/gracefullight/docusaurus-plugins/oma-observability
Any agent
npx skills add gracefullight/docusaurus-plugins --skill oma-observability
Clone the repo
git clone --depth 1 https://github.com/gracefullight/docusaurus-plugins

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for oma-observability

README.md
[![agentmods](https://agentmods.dev/badge/skills/gracefullight/docusaurus-plugins/oma-observability.svg)](https://agentmods.dev/skills/gracefullight/docusaurus-plugins/oma-observability)
Your own site
<a href="https://agentmods.dev/skills/gracefullight/docusaurus-plugins/oma-observability"><img src="https://agentmods.dev/badge/skills/gracefullight/docusaurus-plugins/oma-observability.svg" alt="Measured on agentmods" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,437 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00078 $0.04437
Opus 5 $0.00039 $0.02218
Sonnet 5 $0.00016 $0.00887
Haiku 4.5 $0.00008 $0.00444

Measured yesterday against content hash a13c2edf4ad3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

oma-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 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.

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.

Origin

This is a copy

89% identical to oma-observability — 8 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.

.agents/skills/oma-observability/SKILL.md · 325 lines

How it starts

The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Observability Agent - Intent-based Router

Scheduling

Goal

Route, design, tune, and review observability work across MELT+P signals, layers, boundaries, vendor categories, transport choices, meta-observability, and incident forensics.

Intent signature

  • User asks for observability, telemetry, OTel, metrics, logs, traces, profiles, SLOs, RUM, APM, incident forensics, trace propagation, transport tuning, or observability-as-code.
  • User needs vendor/category routing or observability architecture instead of a single vendor's already-covered setup.

When to use

  • Setting up an observability pipeline (OTel SDK + Collector + vendor backend)
  • Designing traceability across service and domain boundaries (W3C propagators, baggage, multi-tenant, multi-cloud)
  • Tuning transport layer (UDP/MTU, OTLP gRPC vs HTTP, Collector DaemonSet vs sidecar topology)
  • Running incident forensics (6-dimension localization: code / service / layer / host / region / infra)
  • Selecting a vendor category (OSS full-stack vs commercial SaaS vs high-cardinality specialist vs profiling specialist)
  • Implementing observability-as-code (Grafana Jsonnet dashboards, PrometheusRule CRD, OpenSLO YAML, SLO burn-rate alerts)
  • Meta-observability (pipeline self-health, clock skew detection, cardinality guardrails, retention matrix)
  • Covering the MELT+P signal set: metrics, logs, traces, profiles (OTEP 0239), cost (OpenCost), audit (SOC2/ISO), privacy (GDPR/PIPA)
  • Migrating off deprecated tools (Fluentd → Fluent Bit or OTel Collector, per CNCF 2025-10 guide)

When NOT to use

  • LLM ops (prompt versioning, evals, gen_ai span deep dive); use Langfuse, Arize Phoenix, LangSmith, or Braintrust directly
  • Data pipeline lineage: use OpenLineage + Marquez, dbt test, or Airflow lineage backends
  • IoT / hardware / datacenter physical-layer telemetry (IPMI, BMC, SNMP); use vendor DCIM tooling (Nlyte, Sunbird, Device42)
  • Chaos engineering orchestration: use Chaos Mesh, Litmus, Gremlin, or ChaosToolkit (this skill consumes their telemetry; it does not orchestrate chaos)
  • GPU / TPU infrastructure observability: use NVIDIA DCGM Exporter + Prometheus
  • Software supply chain (SBOM, attestation): use sigstore (cosign / rekor), in-toto framework, SLSA level attestations
  • Incident response workflow (on-call rotation, paging, escalation); use PagerDuty, OpsGenie, or Grafana OnCall
  • Single-vendor setup already fully covered by that vendor's own published skill; invoke the vendor skill directly

Read the full file on GitHub · 325 lines

Files

What ships with it

33 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.

Changes

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.

  1. yesterday First seen · 325 lines · 78 tokens per session scan A a13c2edf4ad3

Subscribe to this mod's changes

oma-observability is a skill published in the GitHub repository gracefullight/docusaurus-plugins (22 stars, last pushed 2mo ago), licensed MIT. It adds 78 tokens to every session and 4,437 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to oma-observability, differing in 8 lines, and is treated as a copy.

Analytics and metricsDebuggingadsensechatdocusaurusdocusaurus-plugindocusaurus-plugins
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