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:

Task	Reference
DataPrime query syntax	`references/dataprime-reference.md`
PromQL query syntax, counters vs gauges, histograms	`references/promql-guidelines.md`
Log field discovery, query patterns, wildfind policy	`references/logs-querying.md`
Span field discovery, latency analysis, trace queries	`references/spans-querying.md`
Dashboard-specific query gotchas ( `${__range}` , `promqlQueryType` )	`references/query-syntax.md`
Widget JSON templates	`references/widget-templates.md`

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

To update an existing dashboard:

bash

cx dashboards get <dashboard-id> -o json > dashboard.json
# Edit dashboard.json (change name, modify widgets, etc.)
cx dashboards replace --from-file dashboard.json

To duplicate a dashboard as a new copy:

bash

cx dashboards get <dashboard-id> -o json > dashboard.json
# Remove the "id" field, then create as new:
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: Deploy via `cx dashboards create`
- [ ] Phase 8: 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 7 before Phases 5 and 6 both pass. Phase 8 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 7. 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)

Every PromQL range vector in a metrics widget uses
```
[${__range}]
```
- never
```
[$__range]
```
, never
```
[5m]
```
(unless the panel is intentionally a sliding window).
```
promqlQueryType
```
is
```
PROM_QL_QUERY_TYPE_INSTANT
```
for single-value widgets (gauge, pieChart, dataTable). Omitted for
```
lineChart
```
.
DataPrime log queries use
```
$d.message
```
/
```
$l.applicationname
```
/ unquoted severity enums (full rules:
```
references/dataprime-reference.md
```
).
Every DataPrime widget query starts with
```
source logs
```
or
```
source spans
```
(dashboard widgets require the source prefix; Phase 5 verification strips it before handing the pipeline to
```
cx logs
```
/
```
cx spans
```
).
Success-rate denominators wrapped in
```
clamp_min(..., 1)
```
.
Histogram queries use the correct suffix (
```
_sum
```
,
```
_count
```
,
```
_bucket
```
).
Widget queries are valid without the dashboard-level
```
filters
```
- Coralogix injects them at render time.

Structure

Each section has
```
id.value
```
,
```
rows
```
, and
```
options.custom
```
.
Each row has
```
id.value
```
,
```
appearance.height
```
, and
```
widgets
```
.
Each widget has a unique
```
id.value
```
and a
```
definition
```
with exactly one of
```
gauge
```
/
```
pieChart
```
/
```
lineChart
```
/
```
dataTable
```
.
Success-rate gauges use
```
thresholdType: "THRESHOLD_TYPE_ABSOLUTE"
```
with green at high values; error/DLQ gauges use red at high values.
"Total" / "stat" widgets are encoded as
```
gauge
```
, not as a stat type.
Top-level
```
filters
```
includes each slicing dimension from Phase 2.
All IDs are freshly generated UUIDs, unique within the document.

Content

Dashboard name is descriptive (
```
"<Service> - <Purpose>"
```
).
Widget titles are short, human-readable, and match what the query computes.
The logs/errors section is
```
collapsed: true
```
unless the user said otherwise.

Phase 7: 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 8: 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 7 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

Dashboard query gotchas & cross-references:
```
references/query-syntax.md
```
Widget JSON templates:
```
references/widget-templates.md
```
Live-verification procedure:
```
references/verification.md
```
Deploy procedure:
```
references/deploy.md
```
DataPrime language reference:
```
references/dataprime-reference.md
```
PromQL reference:
```
references/promql-guidelines.md
```
Log querying patterns:
```
references/logs-querying.md
```
Span querying patterns:
```
references/spans-querying.md
```

Inline DataPrime help:

cx dataprime list

cx dataprime show <command>

Coralogix Custom Dashboards docs: https://www.coralogix.com/docs/user-guides/custom-dashboards/introduction/

Related Skills

cx-observability-setup
- full monitoring setup workflow (views, webhooks, notifications, integrations)
cx-incident-management
- SLO and alert-connected dashboards, incident triage
cx-telemetry-querying
- discover the right telemetry signal before building dashboards

cx-dashboards

NPX Install

Tags

SKILL.md Content