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 gracefullight/docusaurus-plugins --skill oma-observabilitygit clone --depth 1 https://github.com/gracefullight/docusaurus-pluginsWrote 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/gracefullight/docusaurus-plugins/oma-observability)<a href="https://agentmods.dev/skills/gracefullight/docusaurus-plugins/oma-observability"><img src="https://agentmods.dev/badge/skills/gracefullight/docusaurus-plugins/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/gracefullight/docusaurus-plugins/oma-observability"><img src="https://agentmods.dev/badge/skills/gracefullight/docusaurus-plugins/oma-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00078 | $0.04437 |
| Opus 5.5 | $0.00031 | $0.01775 |
| Sonnet 5.5 | $0.00016 | $0.00887 |
| Haiku 4.5 | $0.00008 | $0.00444 |
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.
This is a copy
89% identical to oma-observability — 158 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.
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
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 6.5 KB
- resources/checklist.md 15 KB
- resources/examples.md 16 KB
- resources/execution-protocol.md 9.0 KB
- resources/incident-forensics.md 23 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 17 KB
- resources/layers/mesh.md 20 KB
- resources/matrix.md 21 KB
- resources/meta-observability.md 23 KB
- resources/observability-as-code.md 22 KB
- resources/signals/audit.md 14 KB
- resources/signals/cost.md 17 KB
- resources/signals/logs.md 10 KB
- resources/signals/metrics.md 20 KB
- resources/signals/privacy.md 17 KB
- resources/signals/profiles.md 15 KB
- resources/signals/traces.md 19 KB
- resources/standards.md 12 KB
- resources/transport/collector-topology.md 12 KB
- resources/transport/otlp-grpc-vs-http.md 7.6 KB
- resources/transport/sampling-recipes.md 11 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 First seen · 325 lines · 78 tokens per session scan A a13c2edf4ad3
oma-observability is a skill published in the GitHub repository gracefullight/docusaurus-plugins (23 stars, last pushed 27d ago), licensed MIT. It adds 78 tokens to every session and 4,437 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to oma-observability, differing in 158 lines, and is treated as a copy.
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