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 petrkindlmann/qa-skills --skill synthetic-monitoringgit clone --depth 1 https://github.com/petrkindlmann/qa-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/petrkindlmann/qa-skills/synthetic-monitoring)<a href="https://agentmods.dev/skills/petrkindlmann/qa-skills/synthetic-monitoring"><img src="https://agentmods.dev/badge/skills/petrkindlmann/qa-skills/synthetic-monitoring/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/petrkindlmann/qa-skills/synthetic-monitoring"><img src="https://agentmods.dev/badge/skills/petrkindlmann/qa-skills/synthetic-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 318 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00143 | $0.05242 |
| Opus 5 | $0.00072 | $0.02621 |
| Sonnet 5 | $0.00029 | $0.01048 |
| Haiku 4.5 | $0.00014 | $0.00524 |
Grade A, and why
synthetic-monitoring 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 12d 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick Route
| Situation | Go to |
|---|---|
| Picking a platform (Checkly, Datadog, Grafana, CloudWatch…) | Platform Options |
| Writing a probe (login, API health, search) | Probe Design → references/probe-implementations.md |
| Probe runs but users still report outages | Failure Modes |
| Alerts too noisy or missing real outages | Alerting Integration |
| Calculating downtime budget for an SLA | SLA Validation |
| Probe failed at 3 AM and on-call is lost | runbook template in references/platforms-and-ci.md |
Discovery Questions
Check .agents/qa-project-context.md first. If it exists, use it as context and skip questions already answered there. Each answer changes the probe set, the alerting config, or the SLA math.
Critical flows (decides which probes you write):
- What are the 5-10 most important user journeys (login, search, checkout, signup, core workflow)? These become your probe list.
- Which flows, if broken, cause immediate revenue loss or churn? These get the shortest frequency and page on-call.
- Are there flows that break silently (data sync, background jobs, webhooks)? Silent failures need probes most — no user reports them.
- Do you have documented SLAs/SLOs for availability and response time? They set the downtime budget and the alert thresholds.
Current monitoring (decides where the gaps are):
- What exists today (uptime checks, APM, error tracking, dashboards)? Avoid duplicating; find the gap.
- Are there gaps between what monitoring covers and what users experience? That gap is where probes earn their keep.
- How do you learn about production issues today (alerts, user reports, social media)? If it's user reports, detection time is your first metric to fix.
- What was the last outage, and how long before it was detected? Sets the detection-time target probes must beat.
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.
- 12d ago First seen · 348 lines · 143 tokens per session scan A 7598cb011f7b
synthetic-monitoring is a skill published in the GitHub repository petrkindlmann/qa-skills (120 stars, last pushed 3mo ago), licensed MIT. It adds 143 tokens to every session and 5,242 once invoked, about $0.0007 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 2026-08-30.
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