oh-my-agent is a harness for checking whether coding agents actually completed their work by verifying tests, required artifacts, independent reviews, and recorded decisions. It is used across multiple agent runtimes to make workflow results auditable instead of relying on an agent's own report. The catalogue add-ons provide parts of its skills, agents, hooks, MCP integrations, instructions, and plugins.
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 first-fluke/oh-my-agent --skill oma-observabilitygit clone --depth 1 https://github.com/first-fluke/oh-my-agentWrote 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/first-fluke/oh-my-agent/oma-observability)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-observability"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/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.
<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-observability"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-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 | $0.00031 | $0.03475 |
| Opus 5.5 | $0.00012 | $0.01390 |
| Sonnet 5.5 | $0.00006 | $0.00695 |
| Haiku 4.5 | $0.00003 | $0.00347 |
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.
Copies of this mod
2 near-identical copies found in the catalogue:
- oma-observability — 100% identical, 0 lines differ
- oma-observability — 89% identical, 158 lines differ
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
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.
- resources/anti-patterns.md 46 KB
- resources/boundaries/cross-application.md 20 KB
- resources/boundaries/multi-tenant.md 18 KB
- resources/boundaries/release.md 12 KB
- resources/boundaries/slo.md 7.2 KB
- resources/checklist.md 15 KB
- resources/examples.md 17 KB
- resources/execution-protocol.md 9.5 KB
- resources/incident-forensics.md 24 KB
- resources/intent-rules.md 12 KB
- resources/layers/L3-network.md 18 KB
- resources/layers/L4-transport.md 15 KB
- resources/layers/L7-application/crash-analytics.md 18 KB
- resources/layers/L7-application/mobile-rum.md 16 KB
- resources/layers/L7-application/waf.md 17 KB
- resources/layers/L7-application/web-rum.md 18 KB
- resources/layers/mesh.md 20 KB
- resources/matrix.md 21 KB
- resources/meta-observability.md 25 KB
- resources/observability-as-code.md 24 KB
- resources/signals/audit.md 16 KB
- resources/signals/cost.md 18 KB
- resources/signals/logs.md 11 KB
- resources/signals/metrics.md 21 KB
- resources/signals/privacy.md 19 KB
- resources/signals/profiles.md 15 KB
- resources/signals/traces.md 19 KB
- resources/standards.md 13 KB
- resources/transport/collector-topology.md 13 KB
- resources/transport/otlp-grpc-vs-http.md 7.6 KB
- resources/transport/sampling-recipes.md 12 KB
- resources/transport/udp-statsd-mtu.md 6.3 KB
- resources/vendor-categories.md 18 KB
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 · -4 lines · -47 tokens per session c8568b9f5df5
- 1mo ago Changed · -94 lines f2c20cf7897b
- 1mo ago First seen · 325 lines · 78 tokens per session scan A c40a1e367611
oma-observability is a skill published in the GitHub repository first-fluke/oh-my-agent (1,354 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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