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 agentmods add skills/tmj-90/gaffer/observability-designernpx skills add tmj-90/gaffer --skill observability-designergit clone --depth 1 https://github.com/tmj-90/gafferWrote 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/tmj-90/gaffer/observability-designer)<a href="https://agentmods.dev/skills/tmj-90/gaffer/observability-designer"><img src="https://agentmods.dev/badge/skills/tmj-90/gaffer/observability-designer.svg" alt="Measured on agentmods" 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 | $0.00067 | $0.00845 |
| Opus 5 | $0.00034 | $0.00423 |
| Sonnet 5 | $0.00013 | $0.00169 |
| Haiku 4.5 | $0.00007 | $0.00085 |
Grade A, and why
observability-designer 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 6d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design production-ready observability
Instrument services so operators know what is broken, why, and where — before users notice. Three pillars, golden signals, low-noise alerting.
The three pillars
| Pillar | Purpose | Key decision |
|---|---|---|
| Metrics | Rate, latency, saturation at a glance | RED method for services; USE method for resources |
| Logs | Structured event trail with correlation IDs | JSON, log-level discipline, sample high-volume streams |
| Traces | End-to-end request flow across services | Meaningful span boundaries; tail-based sampling for slow/erroring requests |
Golden signals to define first: latency, traffic, errors, saturation — cover these before anything else.
Steps
- Read the lore first.
search_lorefor existing observability decisions (dashboards, alert channels, on-call runbooks, APM tooling). Extend in place; don't duplicate. - Identify the service contract. What does this service promise users? That contract → the SLIs. Route SLO/error-budget design to
slo-architect. - Design dashboards. Overview → service → component drill-down. Max 7±2 panels per screen; colour semantics (red = critical, amber = warning, green = healthy); SLO target reference lines.
- Define alert thresholds. Prefer symptom-based over cause-based alerts. Require every alert to have: condition, severity, runbook link, on-call owner. Suppress during known maintenance.
- Reduce noise. Deduplicate, set appropriate alert-evaluation windows, distinguish pager alerts (must wake someone) from dashboard-only signals.
- Verify + evidence. Deploy to staging, confirm every golden-signal panel renders with live data; run
record-evidencewith test output; submit for review.
Build / Test
- Validate dashboard JSON against the target platform's schema (Grafana, Datadog, CloudWatch) before committing.
- Alert configs: dry-run evaluation against recent telemetry; confirm alert-to-runbook coverage is 1:1.
- Structured logging: emit a test event and confirm all required fields appear in the aggregation layer.
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.
- 6d ago First seen · 57 lines · 67 tokens per session scan A 24db902f962a
observability-designer is a skill published in the GitHub repository tmj-90/gaffer (2 stars, last pushed 6d ago), licensed Apache-2.0. It adds 67 tokens to every session and 845 once invoked, about $0.0003 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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