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 Morningstar202604/awesome-skillkit --skill observability-designergit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote 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/morningstar202604/awesome-skillkit/observability-designer)<a href="https://agentmods.dev/skills/morningstar202604/awesome-skillkit/observability-designer"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/observability-designer/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/morningstar202604/awesome-skillkit/observability-designer"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/observability-designer.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 | $0.00064 | $0.02618 |
| Opus 5 | $0.00032 | $0.01309 |
| Sonnet 5 | $0.00013 | $0.00524 |
| Haiku 4.5 | $0.00006 | $0.00262 |
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 10d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Designer (POWERFUL)
Category: Engineering
Tier: POWERFUL
Description: Design comprehensive observability strategies for production systems including SLI/SLO frameworks, alerting optimization, and dashboard generation.
Overview
Observability Designer creates production-ready dashboards, alert configurations, and monitoring strategies across the three pillars (metrics, logs, traces).
When NOT to use → slo-architect. For SLO/SLI design, error-budget math, multi-window burn-rate alerting thresholds, and SLO review gates — including scaffolding SLO definitions — route to slo-architect (it ships slo_designer.py, error_budget_calculator.py, and slo_review.py). This skill's lane: dashboards (dashboard_generator.py) and alert-noise reduction (alert_optimizer.py).
Quick Start
# Dashboard spec (Grafana JSON + docs) for a service
python3 scripts/dashboard_generator.py --service-type api --name payments --criticality critical --role sre --format grafana -o dashboard.json --doc-output dashboard.md
# Analyze an existing alert config for noise, duplicates, and coverage gaps
python3 scripts/alert_optimizer.py --input alerts.json --analyze-only --report alert_report.json
# ...then emit the optimized config once the report is reviewed:
python3 scripts/alert_optimizer.py --input alerts.json --output alerts_optimized.json
# SLO definitions/error budgets → use the slo-architect skill (scripts/slo_designer.py there)
Verification loop: after deploying optimized alerts, track the report's noise metrics for one on-call rotation — if the actionable-alert ratio didn't improve, re-run --analyze-only against the live config and iterate. Import the generated dashboard into Grafana and confirm every golden-signal panel renders with live data before closing the task.
Core Competencies
SLI/SLO/SLA Awareness (design input — deep work in slo-architect)
- SLI/SLO/SLA definitions and error-budget math → owned by
slo-architect; treat its output as an input here - Burn Rate Alerting: translate a team's SLO burn-rate thresholds into concrete alert rules via
alert_optimizer.py
What ships with it
9 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.
- assets/sample_alerts.json 8.0 KB
- assets/sample_service_api.json 1.7 KB
- assets/sample_service_web.json 2.4 KB
- expected_outputs/sample_slo_framework.json 22 KB
- README.md 9.6 KB
- references/alert_design_patterns.md 12 KB
- references/dashboard_best_practices.md 14 KB
- scripts/alert_optimizer.py 46 KB runs code
- scripts/dashboard_generator.py 48 KB runs code
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.
- 10d ago First seen · 269 lines · 64 tokens per session scan A 7ec6cfffc54f
observability-designer is a skill published in the GitHub repository Morningstar202604/awesome-skillkit (1 stars, last pushed today), licensed Apache-2.0. It adds 64 tokens to every session and 2,618 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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