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Testing

Organization
grafana
testing

Probe, load-test, and instrument frontends from Grafana Cloud. Covers Synthetic Monitoring (HTTP / DNS / TCP / Ping / Traceroute / Multihttp / k6-browser scripted checks from 20+ global probes, alert on `probe_success` + TLS-cert expiry), Grafana Cloud k6 (distributed load tests across AWS load-zones, scenarios, `http_req_duration` thresholds, CI integration via `grafana/k6-action`), and Frontend Observability with Faro Web SDK (RUM, Core Web Vitals, custom events, `pushError`, distributed-trace correlation). Use when checking website / API uptime from multiple regions, gating a release on a load test, watching for TLS-cert renewal, instrumenting a React/Vue app, tracking Core Web Vitals, or correlating frontend errors to backend traces — even when the user says "is my login flow up?", "monitor my API", "ping our endpoint every minute", "release-gate load test", "browser performance monitoring", "session replay", or "RUM" without naming Synthetic Monitoring / k6 / Faro.

Overview

Publishergrafana
Repositoryskills
Skill nametesting
Stars
289
Forks
27
Bundled files
4
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by grafana on GitHub. Read the source before you install it.

Installation

Install the Testing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/grafana/skills/tree/main/skills/grafana-cloud/testing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/grafana/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/grafana-cloud/testing .claude/skills/testing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Testing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Testing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Testing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Grafana Cloud Testing

Docs: https://grafana.com/docs/grafana-cloud/testing/

Three pillars: external probing (Synthetic Monitoring), load testing (k6 Cloud), real-user monitoring (Faro).

Prerequisites

  • Grafana Cloud stack
  • Synthetic Monitoring: SM access token (sm:write)
  • k6 Cloud: a Grafana Cloud k6 token + projectID
  • Faro: a Faro app + write-token from Frontend Observability → Apps

Common Workflows

1. Create + verify a Synthetic HTTP check

bash
# 1. Create the check (full payload in references/synthetic.md)
curl -X POST https://synthetic-monitoring-api.grafana.net/sm/checks \
  -H "Authorization: Bearer <sm-token>" -H "Content-Type: application/json" \
  -d '{"job":"website","target":"https://example.com","frequency":60000,"timeout":15000,
       "enabled":true,"probes":[1,5,10],
       "settings":{"http":{"method":"GET","validStatusCodes":[200]}}}'

# 2. List checks — confirm it exists and is enabled
curl -s https://synthetic-monitoring-api.grafana.net/sm/checks \
  -H "Authorization: Bearer <sm-token>" | jq '.[] | select(.job=="website")'

# 3. Verify probe_success arrived (wait ~60s for the first run)
#    In Grafana Explore on the synthetic metrics datasource:
#      probe_success{job="website"}
#    Expect 1 from each probe in `probes:[1,5,10]`.
#
# Rollback: DELETE /sm/checks/<id> or set `"enabled": false`.

See references/synthetic.md for all check types, PromQL queries, and alert rules.

2. Run a k6 cloud load test

bash
# 1. Authenticate once
k6 cloud login --token <grafana-cloud-k6-token>

# 2. Validate the script locally first (smoke run, no cloud cost)
k6 run --vus 1 --duration 30s script.js

# 3. Launch in cloud
k6 cloud script.js
# → URL of the run; thresholds (p95<500ms, error<1%) decide pass/fail.

# 4. Verify in CI — exit code 0 = thresholds passed, 99 = threshold failed.
echo $?

Full script + scenarios + CI YAML: references/k6-and-faro.md.

3. Instrument a frontend with Faro

bash
# 1. Install
npm install @grafana/faro-web-sdk @grafana/faro-web-tracing
javascript
// 2. initializeFaro({ url, apiKey, app, instrumentations }) — see references/k6-and-faro.md
// 3. Push a test event so we have something to look for:
faro.api.pushEvent('faro_smoketest', { ts: Date.now().toString() });
bash
# 4. Verify the collector accepted it (browser DevTools → Network)
#    POST to /collect should return 202. If 401 — apiKey mismatch.

# 5. Verify in Grafana
#    - Frontend Observability → your app → Sessions: should show your session
#    - Explore on Loki: `{kind="event"} |= "faro_smoketest"`
#    - For traces: filter by `service.name="my-frontend"` in Tempo

Troubleshooting

  • Synthetic check stuck at probe_success=0 → check probe_*_duration_seconds for the failing stage; the probe-region label tells you which prober errored
  • k6 cloud run "ABORTED_THRESHOLD" → a threshold tripped; inspect the run page to see which one
  • Faro events not landing → check the browser network call to /collect returns 202; common cause is wrong apiKey or url (must match the FE Observability app)

References

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Testing AI skill do?

Probe, load-test, and instrument frontends from Grafana Cloud. Covers Synthetic Monitoring (HTTP / DNS / TCP / Ping / Traceroute / Multihttp / k6-browser scripted checks from 20+ global probes, alert on `probe_success` + TLS-cert expiry), Grafana Cloud k6 (distributed load tests across AWS load-zones, scenarios, `http_req_duration` thresholds, CI integration via `grafana/k6-action`), and Frontend Observability with Faro Web SDK (RUM, Core Web Vitals, custom events, `pushError`, distributed-trace correlation). Use when checking website / API uptime from multiple regions, gating a release on...

Why use Testing on TypingMind?

Because you install it once and use it with any model. Testing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Testing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grafana/skills/tree/main/skills/grafana-cloud/testing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Testing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Testing?

As many as you like. As long as a model supports skills, you can use Testing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Testing AI skill free?

Yes. It is published on GitHub by grafana under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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