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 Kin9Zeus/senior-engineer-skills --skill observability-slogit clone --depth 1 https://github.com/Kin9Zeus/senior-engineer-skillsWrote 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/kin9zeus/senior-engineer-skills/observability-slo)<a href="https://agentmods.dev/skills/kin9zeus/senior-engineer-skills/observability-slo"><img src="https://agentmods.dev/badge/skills/kin9zeus/senior-engineer-skills/observability-slo/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/kin9zeus/senior-engineer-skills/observability-slo"><img src="https://agentmods.dev/badge/skills/kin9zeus/senior-engineer-skills/observability-slo.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 | $0.00168 | $0.02589 |
| Opus 5 | $0.00084 | $0.01295 |
| Sonnet 5 | $0.00034 | $0.00518 |
| Haiku 4.5 | $0.00017 | $0.00259 |
Grade A, and why
observability-slo 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 11d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability and Reliability
The question this discipline answers is not "is the server up". It is:
When a user has a bad experience right now, can we tell — and can we find out why without deploying new code?
A system you cannot debug from its own output is a system you debug by guessing.
The three signals, and what each is for
| Signal | Answers | Cost |
|---|---|---|
| Metrics | Is something wrong? Aggregate, cheap, alertable | Low, until cardinality explodes |
| Traces | Where is it wrong? One request across every service | Medium, sample it |
| Logs | Why is it wrong? Full detail for one event | High at volume |
You need all three, and they must be linked: a metric spike leads to an exemplar trace, which leads to the log lines for that request. Three disconnected tools mean three separate investigations and a much longer time-to-diagnosis.
Use OpenTelemetry as the instrumentation layer — it keeps the vendor decision reversible, which matters because observability vendors are expensive and switching is otherwise a rewrite.
Structured logging
{"ts":"2026-08-24T16:18:12.451Z","level":"error","msg":"payment.capture_failed",
"trace_id":"4bf92f...","span_id":"00f067...","request_id":"req_01HQ8",
"user_id":"usr_01HQ8","org_id":"org_01HQ8","payment_id":"pay_01HQ8",
"provider":"stripe","error_code":"card_declined","duration_ms":842}
Rules
- JSON, not prose. Grep does not scale; structured queries do.
- A correlation id on every line, generated at the edge (or accepted from the client), propagated through every service, job and outbound call. This single field is the difference between a two-minute investigation and a two-hour one.
- Log events, not sentences.
payment.capture_failedwith fields beats"Failed to capture payment for user 123"— the first is queryable and aggregatable, the second is a string. - Levels with meaning.
error= a human should look.warn= degraded but handled.info= business events worth keeping.debug= off in production. If everything iserror, nothing is. - Never log secrets or personal data. Tokens, passwords, card numbers, full request bodies, authorization headers. Implement redaction as a layer in the logger, not as a habit at each call site. Logs are frequently the least protected copy of your data, and they are shipped to third parties.
- Log at boundaries: request in/out, external call in/out, job start/end, state transitions. Not every line of business logic.
- Retention bounded deliberately — a cost control and a privacy obligation.
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.
- 11d ago First seen · 248 lines · 168 tokens per session scan A 167ccb87c73c
observability-slo is a skill published in the GitHub repository Kin9Zeus/senior-engineer-skills (3 stars, last pushed 17d ago), licensed MIT. It adds 168 tokens to every session and 2,589 once invoked, about $0.0008 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 2026-08-31.
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Optimize Core Web Vitals (LCP, INP, CLS) for better page experience using field and lab evidence. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts".