| name | cx-dashboards |
| description | Build and deploy a Coralogix dashboard for a given service from its logs, spans, metrics, and service specs. Discovers telemetry via cx CLI commands, emits importable Coralogix JSON, verifies every PromQL and DataPrime query live through the `cx` CLI, and creates or updates dashboards via `cx dashboards create` and `cx dashboards replace`. Use whenever the user asks to create, build, generate, deploy, update, replace, or modify a Coralogix dashboard, monitoring dashboard, or observability dashboard for a service, app, or pipeline.
|
| metadata | {"version":"0.2.0"} |
Create Coralogix Dashboard
Produces a Coralogix dashboard for a target service and deploys it via the cx CLI. Workflow: discover the service's telemetry, align on intent with the user, draft a plan, emit the JSON, live-verify every query through cx, then create the dashboard in a chosen folder.
Only use metric names, log fields, and span attributes you can cite from the service's code, README, configuration, or a live query that returned a result. Do not invent them.
Reference files
Load these files for domain-specific guidance:
For choosing the right signal (metrics / logs / traces), use cx-telemetry-querying.
Dashboard Management
Beyond creating dashboards, use these commands to manage existing ones:
| Command | Purpose |
|---|
cx dashboards catalog -o json | List all dashboards in the catalog |
cx dashboards get <id> -o json | Get a dashboard definition (useful as a template) |
cx dashboards folders list -o json | List dashboard folders |
cx dashboards folders create --name "Name" | Create a dashboard folder |
cx dashboards folders create --name "Sub" --parent-id <id> | Create a nested folder |
cx dashboards replace --from-file dashboard.json | Replace an existing dashboard with updated JSON |
cx dashboards check --from-file dashboard.json | Validate a dashboard definition without persisting (server-side strict check; exits non-zero on errors) |
cx dashboards check <dashboard-id> | Validate a stored dashboard by id |
To update an existing dashboard:
cx dashboards get <dashboard-id> -o json > dashboard.json
cx dashboards replace --from-file dashboard.json
To duplicate a dashboard as a new copy:
cx dashboards get <dashboard-id> -o json > dashboard.json
cx dashboards create --from-file dashboard.json
Workflow
Track progress through this checklist:
Dashboard Progress:
- [ ] Phase 1: Discover telemetry & business meaning
- [ ] Phase 2: Gather dashboard specifications from user
- [ ] Phase 3: Draft internal dashboard plan (sections/rows/widgets)
- [ ] Phase 4: Generate the Coralogix JSON
- [ ] Phase 5: Live-verify every query through the cx CLI
- [ ] Phase 6: Self-verify structure against the checklist
- [ ] Phase 7: Server-side validation via `cx dashboards check`
- [ ] Phase 8: Deploy via `cx dashboards create`
- [ ] Phase 9: Share the dashboard link with the user
Proceed in order. Don't jump to Phase 4 before the user approves the Phase 3 plan, and don't run Phase 8 before Phases 5–7 all pass. Phase 9 is mandatory — the workflow is not done until the user has a clickable link.
Phase 1: Discover telemetry & business meaning
For the target service, gather:
- Business purpose - read
README.md and the top-level entrypoint (main.*, index.*, cmd/main.go, etc.). Summarize in 2–3 sentences what it does, its key stages, and what can go wrong.
- Metrics - for each candidate keyword (service name, subsystem, verbs like
request, error, latency, dlq) run cx metrics search --name '*<keyword>*'. When a metric looks promising, list its labels with cx metrics get-labels <metric>. Only use names cx metrics search returns - this is what prevents invented metrics from reaching Phase 5. Cross-check the service's instrumentation (prometheus_client, promauto.NewCounter/Histogram/Gauge, OTel meters, prom-client, Micrometer, metrics.py) for semantics and histogram buckets (_sum, _count, _bucket).
- Logs - discover custom
$d.* fields with cx search-fields "<description>" --dataset logs before assuming a field exists. Sample message templates and severity with cx logs "filter \$l.applicationname == '<app>'" --limit 5 -o json. Standard fields ($m.severity, $m.timestamp, $l.applicationname, $l.subsystemname) don't need discovery.
- Spans / traces - discover span attributes with
cx search-fields "<description>" --dataset spans. Sample with cx spans "filter \$l.serviceName == '<svc>'" --limit 5 -o json. Error conventions vary ($d.tags.error, $d.http.status_code); check samples before filtering.
- Message buses & DLQs - grep for Kafka, RabbitMQ, SQS, Pub/Sub clients and any
dlq/DLQ references. Note topic/queue names for DLQ panels.
- Service configuration - check
meta.yaml, Helm values.yaml, Deployment, Dockerfile, chart.yaml. Extract:
- The
applicationname / subsystemname label values as they appear in Coralogix.
- Tenant/account/team identifiers used as metric or log labels.
- Deployment environments (
prod, staging, dev, …).
If the signal for a question is ambiguous (e.g. "how much revenue last week"), delegate to cx-telemetry-querying first.
Produce a short internal summary before moving on. If critical telemetry is missing (e.g. no metrics), surface that to the user and ask whether they want a log-only or trace-only dashboard.
Phase 2: Gather dashboard specifications
Ask the user a focused set (≤6). Prefer AskQuestion:
- Audience & use - on-call triage, product/business tracking, capacity planning, customer success?
- Default time range - typical viewing window (e.g. 24h, 7d). Queries still use
${__range} so users can zoom.
- Slicing dimensions - top-level filters (
tenant_id, account_id, subsystem_name, region, env, …).
- Environment scope - which environments to include/exclude (common default: exclude
dev, staging, test).
- SLO-ish signals - success-rate, latency, or throughput targets to highlight?
- Priorities - what to see first (drives row ordering and which section is
collapsed: true).
Don't block on answers you can reasonably infer - state the inference and continue.
Phase 3: Draft the internal plan
Write a markdown plan the user can approve before JSON generation:
## Dashboard: <Service> - <Purpose>
### Section 1: <Overview> (collapsed: false)
- Row 1: [widget type] <title> - <what it shows> - source: metrics|logs|spans
- Row 2: ...
### Section 2: <Deep dive> (collapsed: false)
...
### Section N: <Logs & errors> (collapsed: true)
...
### Top-level filters
- <label> (<source>)
### Assumptions / gaps
- ...
Section design:
- First section: at-a-glance health (gauges + key rates), always expanded.
- Pair related time-series in the same row (rate + latency).
- Final section (raw logs, rare breakdowns):
collapsed: true.
- Aim for 3–5 sections, 6–20 widgets total.
Widget-type selection:
| Signal | Widget type |
|---|
| Single headline number (count, % success, totals) | gauge (Coralogix calls this "stat") |
| Breakdown across ≤8 categories | pieChart |
| Change over time (rate, latency, count per bucket) | lineChart |
| Top-N tables, last errors, per-entity listings | dataTable |
Don't use other widget types unless the user asks.
Wait for the user to approve or adjust the plan before emitting JSON.
Phase 4: Generate the Coralogix JSON
Produce a single JSON document following references/widget-templates.md. Key rules:
- Top-level shape:
{
"id": "<21-char-nanoid>",
"name": "<Dashboard Name>",
"layout": { "sections": [ ... ] },
"variables": [],
"variablesV2": [],
"filters": [ ... ],
"relativeTimeFrame": "<seconds>s",
"annotations": [],
"off": {},
"actions": []
}
- IDs - fresh UUIDs for every
section, row, widget, and query id.
- Row height -
"appearance": { "height": 19 } unless there's a reason to change.
- Section options - include
options.custom.name, collapsed, and color.predefined: "SECTION_PREDEFINED_COLOR_UNSPECIFIED".
- Filters - one entry per slicing dimension from Phase 2. Default operator
equals with empty values so users can fill in. Use notEquals for environment exclusions (see references/widget-templates.md).
- relativeTimeFrame - default
"172800s" (48h) unless the user specified otherwise.
For query syntax follow references/query-syntax.md; for the full query languages load references/dataprime-reference.md and references/promql-guidelines.md.
Phase 5: Live-verify every query through the cx CLI
Every PromQL and DataPrime query in the draft has to successfully run through cx before Phase 8. This catches invented metric names, typoed field paths, and malformed pipelines.
Frequent vs Archive (what / when / where in JSON)
What:
- Frequent (
TIER_FREQUENT_SEARCH): hot tier for fast search on recent logs/spans.
- Archive (
TIER_ARCHIVE): cold tier for older logs/spans (long-term).
When to choose:
- Choose Frequent for on-call and recent investigations (hours/days).
- Choose Archive for long lookbacks (weeks/months) or when the time range is beyond hot retention.
The two languages are verified against different windows:
- PromQL: map
relativeTimeFrame to a $RANGE token (e.g. 48h for 172800s), substitute ${__range} with [$RANGE] for the CLI call, then restore ${__range} in the JSON before Phase 6. Range vectors are window-sensitive, so the check has to match what the dashboard will evaluate.
- DataPrime: verify against a fixed short window (
now-15m → now, --limit 1). The goal is syntax / field / pipeline validation, not data-presence on the dashboard's window — a short window is faster and a cleaner fail signal.
Full procedure (CLI invocations, $RANGE mapping table, retry budget, failure modes): references/verification.md.
If a query can't be made to pass within the retry budget, surface it to the user with the CLI error verbatim - don't ship a broken widget.
Phase 6: Self-verify structure
Run this checklist against the final JSON. Fix and re-check if any item fails before Phase 7.
Query syntax (dashboard-specific)
Structure
Content
Phase 7: Server-side validation via cx dashboards check
Phase 6 caught structural issues by hand. This phase runs the whole dashboard through the Coralogix Dashboard Service's strict validator (CheckDashboard) — the same validation create/replace apply on write, plus a superset that compiles every PromQL/DataPrime query and enforces required ids for variables and filters.
- Run
cx dashboards check --from-file /tmp/cx-dashboard-<slug>.json.
- If the command exits non-zero, the output lists issues with
severity, location (an RFC 6901 JSON Pointer into the dashboard, e.g. /sections/0/rows/1/widgets/2), and message. Fix each issue in the JSON (loop back to Phase 4), re-run Phase 5 for any query that changed, then re-run this phase.
- When
check exits 0 (text output: Dashboard is valid (no issues)), proceed to Phase 8.
check is read-only — it never persists the dashboard. Warnings (SEVERITY_WARNING) print but do not fail the gate; only errors (SEVERITY_ERROR) cause a non-zero exit. In multi-profile fan-out, any profile returning errors fails the command.
Phase 8: Deploy via cx dashboards create
Don't tell the user to paste JSON into the Coralogix UI - deploy it directly.
- List folders:
cx dashboards folders list -o json.
- Suggest the best folder match (team, product area, or a folder named after the service). Default to root (omit
--folder) if nothing fits.
- Write the verified JSON to a temp file and run
cx dashboards create --from-file /tmp/cx-dashboard-<slug>.json --folder <id>. The CLI generates the requestId envelope and prints the created dashboard ID.
Full procedure (folder-picking UX, command templates, idempotency note): references/deploy.md.
On failure: show the CLI error verbatim and return to Phase 5. The most common cause is a query that parses locally but the live API rejects.
Phase 9: Share the dashboard link
The workflow is not done until the user has a clickable link to the dashboard. Printing the ID alone forces the user to navigate the Coralogix UI by hand, which defeats the point of automating deployment.
After Phase 8 succeeds, capture the dashboard id returned by cx dashboards create, build the URL using the region → webapp host mapping in references/deploy.md § "Share the link", and emit the output template below. Render the dashboard name as the link text — that's what the user clicks.
Output format for the user
When the webapp host is resolvable (any built-in region, or a Custom endpoint that follows the api.<host> convention):
## Plan
<the approved Phase 3 plan>
## Verification
- PromQL queries verified: <N>/<N>
- DataPrime queries verified: <N>/<N>
## Deployed
- Dashboard: **[<Name>](https://<region>.app.coralogix.com/#/dashboards/<id>)**
- ID: `<id>`
- Folder: `<folder name or "root">`
- Profile: `<cx profile>`
Open it: [<Name>](https://<region>.app.coralogix.com/#/dashboards/<id>)
Adjust filter values (e.g. `account_id`) after opening it.
When the webapp host cannot be derived (custom endpoint that doesn't match api.<host>), omit the link entirely — do not invent a URL. Use this template instead:
## Plan
<the approved Phase 3 plan>
## Verification
- PromQL queries verified: <N>/<N>
- DataPrime queries verified: <N>/<N>
## Deployed
- Dashboard: **<Name>** (open via the Coralogix UI; ID `<id>`)
- ID: `<id>`
- Folder: `<folder name or "root">`
- Profile: `<cx profile>`
Adjust filter values (e.g. `account_id`) after opening it.
References
Related Skills
cx-observability-setup - full monitoring setup workflow (views, webhooks, notifications, integrations)cx-slos - SLO-connected reliability targets to surface on dashboardscx-cases - triage the cases raised against the services a dashboard monitorscx-telemetry-querying - discover the right telemetry signal before building dashboards
