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 atretyak1985/swarmery --skill observabilitygit clone --depth 1 https://github.com/atretyak1985/swarmeryWrote 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/atretyak1985/swarmery/observability)<a href="https://agentmods.dev/skills/atretyak1985/swarmery/observability"><img src="https://agentmods.dev/badge/skills/atretyak1985/swarmery/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/atretyak1985/swarmery/observability"><img src="https://agentmods.dev/badge/skills/atretyak1985/swarmery/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 | $0.00041 | $0.00764 |
| Opus 5 | $0.00020 | $0.00382 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Instrument the project's services with structured logging and OpenTelemetry distributed tracing, and provide the patterns for correlating logs, traces, and metrics when diagnosing issues. Code-level instrumentation only — what to log, how to propagate trace context, how to correlate signals. Placeholders <mainApp> and <device> resolve from project.json.
Rules
- Logs are JSON with required fields
timestamp,level,service,message— plustrace_id/span_idwhen tracing is active. - NEVER log PII, passwords, tokens, API keys, or secrets; review every context field.
- High-frequency per-message events (several Hz per device) log at DEBUG, never INFO; INFO is for summaries and state changes.
- Span names are meaningful with domain-specific attribute keys (
device.id, notid); no unbounded span attributes (request bodies, user input). - Propagate trace context at every service boundary (WebSocket, HTTP) — a missing injection breaks the trace and loses correlation.
- Logging and tracing only: metrics, dashboards, and alerts belong to
monitoring; Helm health probes and log routing belong to the project's infra pack skills.
Resources
- Read
resources/logging-and-tracing-patterns.mdwhen instrumenting — the log format, Python/TypeScript logger wrappers, OTel span code, context propagation, the five-step correlation path, inputs/outputs and length budgets. - Read
resources/checks-and-failure-modes.mdbefore returning or when something misbehaves — self-check list, common mistakes, escalation triggers, worked examples, failure modes, related skills.
How to use
What it does
Adds structured logging and OpenTelemetry tracing to a service and shows how to follow one request across services: which fields every log line carries, how to name spans, how to pass trace context over a boundary, and how to jump from a slow trace to the log lines explaining it.
When to use it
- Adding logging to a service and wanting a JSON format a log backend can parse.
- Instrumenting with OTel spans, or a trace breaks mid-flow and needs context propagation.
- Going from a trace ID to the log lines for that same request.
- Reviewing log statements for leaked personal data, tokens, or secrets. (Metric/dashboard/alert work starts with
monitoringinstead.)
What ships with it
2 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.
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.
- 9d ago First seen · 59 lines · 41 tokens per session scan A 8f791273faab
observability is a skill published in the GitHub repository atretyak1985/swarmery (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 41 tokens to every session and 764 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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