What is oma-observability?

An observability design and routing assistant for telemetry such as metrics, logs, traces, profiles, application monitoring, and user-experience monitoring.

Use it to design OpenTelemetry pipelines, trace requests across service boundaries, tune telemetry transport, define observability as code, select monitoring tools, and investigate incidents.

It helps choose and connect the right monitoring approach across services, infrastructure, vendors, and incident investigations.

oma-observability — Skill for Claude Code and Codex by first-fluke

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.

Any agent

npx skills add first-fluke/fullstack-starter --skill oma-observability

Clone the repo

git clone --depth 1 https://github.com/first-fluke/fullstack-starter

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/first-fluke/fullstack-starter/oma-observability/github.svg)](https://agentmods.dev/skills/first-fluke/fullstack-starter/oma-observability)

Your own site

<a href="https://agentmods.dev/skills/first-fluke/fullstack-starter/oma-observability"><img src="https://agentmods.dev/badge/skills/first-fluke/fullstack-starter/oma-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/first-fluke/fullstack-starter/oma-observability"><img src="https://agentmods.dev/badge/skills/first-fluke/fullstack-starter/oma-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,475 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% 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.

Model	Per session	Once invoked
Fable 5.1	$0.00031	$0.03475
Opus 5.5	$0.00012	$0.01390
Sonnet 5.5	$0.00006	$0.00695
Haiku 4.5	$0.00003	$0.00347

Measured 1mo ago against content hash c8568b9f5df5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 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.

Origin

This is a copy

100% identical to oma-observability — 0 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 · 227 lines

How it starts

The opening of the file, as written. The whole thing — 227 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)
Evaluating log-pipeline migrations against current upstream support and project requirements

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
Full TLS packet inspection: use packet-analysis or vendor TLS inspection tooling
Single-vendor setup already fully covered by that vendor's own published skill; invoke the vendor skill directly

Read the full file on GitHub · 227 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.

1mo ago Changed · -98 lines · -47 tokens per session c8568b9f5df5
1mo ago First seen · 325 lines · 78 tokens per session scan A c40a1e367611

Subscribe to this mod's changes

oma-observability is a skill published in the GitHub repository first-fluke/fullstack-starter (224 stars, last pushed 7d ago), licensed MIT. It adds 31 tokens to every session and 3,475 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to oma-observability, differing in 0 lines, and is treated as a copy.

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