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/summarybotng/summarybot-ng/observability-testing-patternsnpx skills add summarybotng/summarybot-ng --skill observability-testing-patternsgit clone --depth 1 https://github.com/summarybotng/summarybot-ngWrote 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/summarybotng/summarybot-ng/observability-testing-patterns)<a href="https://agentmods.dev/skills/summarybotng/summarybot-ng/observability-testing-patterns"><img src="https://agentmods.dev/badge/skills/summarybotng/summarybot-ng/observability-testing-patterns.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 | $0.00047 | $0.07973 |
| Opus 5 | $0.00023 | $0.03986 |
| Sonnet 5 | $0.00009 | $0.01595 |
| Haiku 4.5 | $0.00005 | $0.00797 |
Grade A, and why
observability-testing-patterns 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 today.
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 — 931 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Testing Patterns
<default_to_action> When testing observability infrastructure, dashboards, or monitoring:
- VALIDATE data accuracy (source data matches what the dashboard displays)
- TEST alert rules fire correctly at defined thresholds
- VERIFY log aggregation completeness (no missing logs across services)
- TRACE distributed requests end-to-end through APM
- MEASURE dashboard performance (render time, query latency)
- CONFIRM SLA/SLO compliance through synthetic monitoring
- TEST metric pipeline integrity from collection to display
Quick Pattern Selection:
- Dashboard shows wrong numbers -> Data accuracy validation
- Alerts not firing -> Alert rule threshold testing
- Missing logs in Kibana -> Log aggregation completeness
- Slow dashboard -> Dashboard performance testing
- Broken traces -> APM trace validation
- SLA disputes -> SLO compliance validation
Critical Success Factors:
- Observability is only as good as the data it shows
- A dashboard that lies is worse than no dashboard
- Alert fatigue kills response times; test thresholds carefully </default_to_action>
Quick Reference Card
When to Use
- Validating dashboard data accuracy (Kibana, Grafana, Datadog)
- Testing alert rule thresholds and notification delivery
- Verifying log aggregation completeness across microservices
- Validating distributed tracing (APM) correctness
- Measuring SLA/SLO compliance
- Testing metric pipeline integrity (collection -> aggregation -> display)
Testing Levels
| Level | Purpose | Dependencies | Speed |
|---|---|---|---|
| Query Validation | Elasticsearch/PromQL query accuracy | Data source | Fast |
| Dashboard Accuracy | Visual matches source data | Full stack | Medium |
| Alert Threshold | Trigger and notification testing | Alerting stack | Medium |
| Pipeline Integrity | End-to-end metric flow | Full pipeline | Slower |
| Performance | Dashboard render time, query latency | Full stack | Slower |
Critical Test Scenarios
| Scenario | Must Test | Example |
|---|---|---|
| Data Accuracy | Dashboard = source truth | Order count on dashboard = DB count |
| Alert Firing | Threshold triggers alert | Error rate > 5% fires PagerDuty |
| Alert Recovery | Auto-resolve when recovered | Error rate drops below 5% clears alert |
| Log Completeness | All services emit logs | 10 microservices, all logs in Kibana |
| Trace Integrity | Full request path visible | Auth -> API -> DB -> Cache spans |
| SLO Compliance | Error budget tracking | 99.9% availability over 30 days |
| Time Accuracy | Timestamps aligned | Log timestamp matches event time |
What ships with it
3 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.
- today First seen · 931 lines · 47 tokens per session scan A 45fb1502149c
observability-testing-patterns is a skill published in the GitHub repository summarybotng/summarybot-ng (2 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 7,973 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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