1Examples: choose a starter example or paste your own source.
2Review the generated preview and adjust labels, conditions, or configuration details.
3Copy the source or export the rendered result when it is ready for documentation.

Common use cases

README and engineering design docsArchitecture reviews and API documentationAI-generated diagram validation before publishing

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FAQ

Can I use this with AI-generated content?

Yes. Use AI for a first draft, then preview and adjust the result before publishing it in documentation.

Use this tool when you need a fast browser workflow for developer documentation, architecture notes, runbooks, API specs, and AI-generated diagrams.

Start with one of the examples, replace it with your own source, and keep the generated output next to the original text so changes stay reviewable.

For production documentation, export SVG or PNG when available and keep the source text in your repository for future edits.

Demo: generate an observability starter pack

An observability pack is strongest when dashboard panels, alert rules, architecture context, and runbook steps are generated together for the same service.

Include service name, runtime, dependencies, and SLO target in the prompt.
Generate dashboards and alerts from the same metric vocabulary.
Review runbook actions before using the pack for on-call work.

Service: checkout-api
Runtime: Kubernetes
Metrics: Prometheus http_requests_total, duration buckets
Logs: Loki by service label
SLO: 99.9% successful checkout requests

Operations checklist: generated observability needs tuning

Generated dashboards and alerts should be imported into a test folder first. Metric labels, datasource UIDs, severities, and thresholds must match your environment.

Replace placeholder datasource IDs.
Tune alert thresholds with historical data.
Add owner, escalation, and runbook links before paging.

dashboard folder: draft
alert severity: ticket -> page after validation
runbook: /runbooks/checkout-api

Review checklist for Observability Pack Generator

Use Observability Pack Generator when you need to inspect source content visually before it becomes documentation, a pull request note, an incident write-up, or a handoff artifact. Generate Grafana dashboard YAML, Prometheus rules, Mermaid architecture, and a runbook checklist with AI.

Before exporting, check that labels are readable, relationships match the source, generated examples do not contain private data, and the preview still makes sense after you edit the input.

Limits and troubleshooting

If the preview fails, reduce the input to the smallest complete example, confirm the format syntax, and then add sections back one at a time. Many rendering failures come from partial files, indentation mistakes, missing diagram headers, or copied snippets that depend on hidden context.

Treat the preview as a review surface rather than a source of truth. Generated diagrams, converted files, dashboards, and rule examples should be checked before they are used in production documentation or operations.

Example inputs to test

API service: Generate an observability pack for a checkout API with p95 latency, error rate, saturation, Redis dependency, and payment provider failures.
Kubernetes app: Create an observability pack for a Kubernetes web app with ingress, deployment replicas, pod restarts, CPU, memory, logs, and SLO burn alerts.
Queue worker: Generate dashboards and alerts for an order worker that consumes Kafka, writes PostgreSQL, and must monitor lag, retries, dead letters, and processing time.

Tool maturity

AI beta

AI-assisted beta

This tool can generate editable source with an AI provider. Review the output, render it locally, and adjust details before publishing.

The maturity label is not a quality score. It tells visitors whether the tool is best for stable export, deeper debugging, quick parsing, or AI-assisted generation.