dashboard-management

name	dashboard-management
description	How analytics dashboards are stored, created, and modified. Covers SQL dashboard tables, legacy settings migration, folders, valid panel sources, layout shape, and safe update patterns. Use when creating, organizing, sharing, or modifying Analytics dashboards.

Dashboard Management

Dashboards are the canonical SQL-backed Analytics resources. New dashboards, saved analyses, reports, and bespoke extensions are represented as dashboard artifacts—not as separate user-facing Analytics objects. Legacy analysis tables and actions remain available only for compatibility.

Storage

Current storage:

Table	Purpose
dashboards	Explorer and SQL dashboard records
dashboard_folders	Personal and shared SQL dashboard folders
dashboard_views	Saved filter presets per dashboard
dashboard_shares	Standard framework share grants
dashboard_folder_shares	Standard folder share grants
dashboard_revisions	Bounded dashboard history snapshots
analyses	Saved ad-hoc analysis records
analysis_revisions	Bounded analysis history snapshots
analysis_shares	Standard framework share grants for analyses

Legacy settings keys such as u:<email>:dashboard-*, u:<email>:sql-dashboard-*, o:<orgId>:sql-dashboard-*, and adhoc-analysis-* are still read as a fallback and copied into SQL on access. Do not create new dashboard settings rows.

Dashboard folders

Dashboard folders are SQL-backed, access-scoped containers for organizing SQL dashboards from /dashboards:

• personal folders are private to their owner.
• shared folders are organization-visible and require an active organization.
• Use list-dashboard-folders, create-dashboard-folder, and set-dashboard-folder for folder reads and membership changes.
• A folder never expands dashboard access. A personal folder can contain only an owned private dashboard; a shared folder can contain only an org-visible dashboard. Use the normal dashboard sharing actions separately.

For organization-wide consolidation, use migrate-analytics-artifacts first with dryRun: true. The write requires an organization owner/admin and the exact confirmation token MIGRATE_ANALYTICS_ARTIFACTS. It materializes organization-scoped legacy settings, creates dashboard blocks for saved analyses and standalone extensions, archives exact duplicates, copies shares, and removes legacy settings keys only after SQL rows are materialized. Source rows remain recoverable. Private member-only rows are intentionally outside this organization-scoped operation.

Use mutate-dashboard for existing dashboard edits. It resolves the current user/org context, validates the resulting config, writes the SQL-backed record, syncs collab, and returns compact proof. Use update-dashboard for new full-config saves, UI full-config saves, or explicitly requested low-level JSON-pointer edits.

Every meaningful dashboard save snapshots the previous state into dashboard_revisions. Use list-dashboard-revisions to inspect available undo points and restore-dashboard-revision to restore one; restore snapshots the current state first, then syncs open dashboard editors.

Saved analyses follow the same undo model with analysis_revisions, list-analysis-revisions, and restore-analysis-revision.

Never use db-patch, raw SQL, or settings-key edits to create or modify a dashboard config. Those bypass the dashboard action's access checks, SQL validation, collab sync, and proof-of-done return. If a dashboard action fails because the argument shape was wrong, fix that action's arguments and retry once — do not switch to db-patch or raw SQL.

Valid Panel Sources

panel.source is a backend selector, not a table name. It must be one of:

Source	Query shape
bigquery	Literal warehouse SQL. Table names belong inside the SQL string.
ga4	JSON descriptor for the Google Analytics Data API.
amplitude	JSON descriptor for an Amplitude query.
first-party	Read-only SQL over this template's analytics_events table, usually via query-agent-native-analytics.

Do not use app-db as a dashboard source. For first-party events collected through /track, use source: "first-party" or the query-agent-native-analytics action rather than raw internal db-query.

AI-generated first-party panels are dashboard-time-bound by default. Set config.timeScope to "dashboard" and include the matching dashboard time filter in the SQL. The allowed values are:

• dashboard: use the dashboard-selected time range; the default for ordinary metrics.
• fixed-window: use an explicit bounded window independent of the dashboard filter.
• cohort-history: use the bounded history of an explicitly defined cohort.
• all-time: scan all available history; use only when the user requests it and put all-time, lifetime, or historical in the title or description.

{{timeRange}} requires an explicit matching filters entry with id: "timeRange" and type: "select". {{<id>Start}} and {{<id>End}} require a matching filters entry with that id and type: "date-range". Do not rely on undeclared time variables. Server validation rejects unbound first-party SQL, so declare the filter or choose an explicit non-dashboard scope before saving.

A bound anywhere in the SQL is not the same as every CTE having its own bound. If a panel has multiple top-level CTEs (WITH a AS (...), b AS (...)) and more than one of them reads analytics_events, EVERY one of those CTEs needs its own {{timeRange}}/{{<id>Start}}/{{<id>End}} reference or literal date bound — not just the final SELECT or one sibling CTE. A CTE that computes something like "this user's first-ever active day" by scanning analytics_events with no bound at all will full-table-scan on every render even though the panel looks time-bound overall (root cause of a 2026-07-25 production incident: several dashboards had exactly this shape). Server validation checks each top-level CTE independently now, so this fails at save time — but write it right the first time: bound every CTE, or use config.timeScope: "cohort-history" only for a CTE that is genuinely defining a cohort (e.g. a first-seen date), never as a way to skip bounding an ordinary activity scan.

Creating A Dashboard

When the user asks for a dashboard:

1. Read the injected <data-dictionary> block first (catalog-first). If relevant entries exist, use their table, columns, queryTemplate, and gotchas verbatim.
2. If a metric definition, date range, or grain is ambiguous and the choice would change the panel's numbers, use the ask-question clarifying tool once before building. Skip it when the dictionary or the user already settled it.
3. If a metric is not documented, do not guess column names. Ask for the table/columns or introspect the provider schema, then propose a dictionary entry with save-data-dictionary-entry.
4. Build a complete SqlDashboardConfig with name and panels. Optionally set top-level columns (1–6, default 2) to control how many grid columns the panels before any section use.
5. Every panel needs id, title, source, chartType, width, and sql. width is the number of grid columns the panel spans (1..6, clamped to the active section's column count). Section panels skip source and sql and may set their own columns (1–6) to override the dashboard default for the panels following the section. Extension panels (chartType: "extension") also skip source and sql; use config.extensionId for ordinary author-selected shared embeds. Use config.extensionSlotId only when the user explicitly asks for a personal/per-viewer slot (see "Embedding An Extension As A Panel").
6. Persist with update-dashboard, not raw SQL or settings writes.
7. Navigate to it with pnpm action navigate --view=adhoc --dashboardId=<id>.

An explicit dashboard request authorizes the complete non-destructive build in the same turn. After querying or scaffolding, continue through extension-data seeding/refresh, dashboard save/embed, and navigation; do not ask whether to proceed or leave an empty Custom Block shell. Ask only when metric scope/grain is materially ambiguous, the change is destructive, or it has an external side effect such as sending email or outreach.

Layout is always 1 column when the available content width is below the md threshold (panels stack), then expands to the configured column count at/above it. The grid uses a container query, so it also stacks when the agent sidebar narrows the content pane — not only at narrow viewports. So picking 3 or 4 columns is fine — the renderer keeps narrow layouts readable automatically.

pnpm action update-dashboard --dashboardId weekly-metrics --config '<full json>'
pnpm action navigate --view=adhoc --dashboardId=weekly-metrics

The save path dry-runs BigQuery panels before persisting. If validation returns a provider error, fix the query and retry. Never work around validation by writing directly to a table.

Dual-Axis Charts

line, area, and bar panels can plot series against two y-axes. Reach for this whenever series share an x-axis but not a unit — a count next to a rate, or revenue next to a conversion percent. On a single axis the smaller series flattens into the baseline and reads as "no data."

{
  "id": "signups-vs-conversion",
  "title": "Signups vs conversion rate",
  "source": "first-party",
  "chartType": "line",
  "width": 1,
  "sql": "SELECT day, signups, conversion_rate FROM ...",
  "config": {
    "timeScope": "dashboard",
    "xKey": "day",
    "yKeys": ["signups", "conversion_rate"],
    "yFormatter": "number",
    "rightYKeys": ["conversion_rate"],
    "rightYFormatter": "percent"
  

• rightYKeys names series from yKeys; everything unnamed stays on the left.
• rightYFormatter defaults to yFormatter when omitted.
• Each axis is labelled with its series names (up to two per side), and tooltip values use the formatter of the axis the series belongs to.
• At least one series must remain on the left. If rightYKeys names every series, or names a column the query never returned, the panel falls back to a single axis and shows a config warning rather than dropping the series.
• Scheduled email reports render the same two scales, so a dual-axis panel is safe to put on a subscribed dashboard.

Reusable Native Dashboard Patterns

The recent extension-backed dashboards in Builder Analytics cluster into a few repeatable compositions. Prefer these native panels, with a real SQL or Data Program result behind each one, when creating a replacement or a new dashboard:

Pattern	Native composition
Customer ROI / value realization	metric KPI cards, line or area trends, table detail, and callout or section panels for the business narrative
Account engagement / outreach	metric coverage and adoption cards, a daily line trend, heatmap or table segmentation, and callout alerts
GTM pipeline / cross-sell	funnel for ordered stages, metric totals, bar or line trends, and a table for account-level follow-up
Win/loss analysis	section groups with metric, table, bar, callout, and trend panels; use a Data Program for provider joins and evidence rows

Funnel panels use config.xKey for the stage label and config.yKey for a non-negative count or value. The renderer preserves the SQL row order, shows each stage's share of the first stage, and shows the change from the previous stage. Keep the intended stage order in SQL with ORDER BY.

When a dashboard is being migrated from an extension, create a new v2 copy, bind its panels to the real provider schema or Data Programs, and compare it with the original before retiring the extension-backed version. Do not invent customer-specific SQL, provider joins, cached rows, or extension ids in a catalog template. Existing dashboards remain readable while the native replacement is validated. Bespoke interaction flows, arbitrary layouts, and visualizations outside these contracts may remain Custom Blocks.

The source tree ships four provider-free v2 manifests in server/lib/native-v2-dashboards.ts: Customer ROI, Account Engagement, Cross-sell, and Win / Loss. They intentionally contain no customer names, provider ids, SQL, cached rows, or guessed joins. After deployment, an organization owner or admin provisions them with ensure-native-v2-dashboards by supplying one real Data Program per binding key. The action validates the stored program output contract, shares the programs with the organization, creates deterministic native-*-v2-* dashboard copies, and preserves the extension-backed originals and any existing v2 edits. Do not add these to the root demo bootstrap or silently auto-bind them to guessed provider schemas.

When To Use An Extension Instead

Native Analytics dashboards are JSON configs rendered by the built-in dashboard components. Use native dashboard actions only when the request fits that model: standard panels, supported chart types, filters, variables, sections, and grid layout. Dual-axis charts are part of that model — build one with config.rightYKeys, never as an extension.

If the user asks for a dashboard or analytical surface that needs bespoke UI or code beyond the dashboard JSON/component model, create an extension and embed it in the dashboard. Examples include custom interaction flows, non-standard visualizations, complex multi-step workflows, highly custom layouts, custom client-side state, or a dashboard-like app that needs behavior the built-in renderer cannot express. In production mode, call create-extension automatically, then call update-dashboard with one or more chartType: "extension" panels using config.extensionId. Never leave the extension as a standalone Analytics result or direct the user to the Extensions page.

Embedding An Extension As A Panel

Use chartType: "extension" to add an extension box alongside normal SQL charts. The panel skips source and sql. For ordinary requests such as "put X in this dashboard," save the author-selected extension id in config.extensionId. This makes the selection part of the shared dashboard and keeps the widget present in scheduled report captures:

{
  "id": "pipeline-widget",
  "title": "Pipeline Widget",
  "chartType": "extension",
  "width": 3,
  "config": { "extensionId": "extension-123" },
}

Direct embeds receive the dashboard id, name, description, current filters, and panel context. Embedding does not grant extension access, so share the extension with the dashboard audience.

Use a stable config.extensionSlotId only when the user explicitly wants each viewer to choose or install their own widget:

analytics.dashboard.<dashboard-id>.panel.<panel-id>

Create or choose the extension, call add-extension-slot-target with the extension id and slot id, then call install-extension with the same values. The dashboard panel is shared, while the installed extension is per-user. Empty slots show the normal install affordance instead of a broken iframe.

{
  "id": "pipeline-widget",
  "title": "Pipeline Widget",
  "chartType": "extension",
  "width": 3,
  "config": {
    "extensionSlotId": "analytics.dashboard.weekly-metrics.panel.pipeline-widget",
  },
}

Notes:

• Both direct and slot-backed extensions receive dashboard and panel context.
• Installs and extension access are per viewer. Sharing the dashboard does not automatically install or grant access to its extension for other viewers.
• Slot installs are per-user preferences. Different viewers can see different widgets, and scheduled reports running as a service identity may show an empty slot. This is why slots are opt-in rather than the default.

Cloning A Direct-Extension Dashboard (e.g. per-customer copies)

When the user asks for a copy of an existing extension-backed dashboard for a different customer/org (for example "make an Intuit version of the Roku usage dashboard"), follow this playbook. Extension bodies are frequently tens of thousands of characters. The reliable path is to read+transform+write the body INSIDE run-code (where workspaceRead returns the full file) and then create from that written file — never by pulling the body into chat context first or re-typing it as a content argument.

1. get-sql-dashboard with includeConfig: true on the source dashboard and confirm the target panel is a chartType: "extension" panel with config.extensionId; grab that extension id. For a slot-backed panel, clone the dashboard panel with a new stable extensionSlotId, then target and install the desired extension into that slot instead of using this body-copy playbook.
2. get-extension for that id with forceContent: true exactly once. Reuse that body for the rest of the turn — a second same-run read intentionally omits content and returns contentOmitted instead. That is not the content disappearing; use the copy you already have. Do NOT try to re-fetch the body with run-code (appAction('get-extension')) to page past a display truncation — the same-run omit makes it return empty content, wasting turns. If you need the full body again, read the workspace resource file (step 5) or set forceContent: true on a single native get-extension.
3. Change ONLY the small customer-specific static config (e.g. the ACCOUNT_USAGE_STATIC block: company name, title, org-discovery filters, messaging). Prefer a focused update-extension edit/patch over regenerating the entire HTML.
4. Call create-extension / update-extension as native tools. They are mutating actions and are NOT callable from run-code / appAction (the sandbox bridge only exposes read-only actions). Do not try to create or update an extension from inside run-code.
5. If the source body already exists as a workspace/shared resource file (e.g. a pre-built intuit-analytics-extension.html), do the read AND the customer swap in ONE run-code call, then create from the written file:
   • Inside run-code: const src = await workspaceRead('<source>.html') returns the WHOLE file (it auto-pages; there is no 50k cap here), do the small string-replace on the static config block, then await workspaceWrite('<target>.html', modified).
   • Then call create-extension (native) with contentFromWorkspaceFile: '<target>.html' and leave content empty — the server reads the full file verbatim. Do NOT read the source body with the read tool (or ) first just to transform it: that display is capped and wastes a turn. And do NOT re-emit an 80k+ char body as the argument — it gets cut off mid-stream. only sees files the user pasted into chat, not workspace resources. / are mutating and cannot run from , so only the read+write+transform happens there.

Repairing An Existing Extension-Backed Dashboard

When the user asks to fix data loading in an existing or migrated extension-backed dashboard, treat the current extension body as user-authored design. Read the dashboard config and extension once, identify the smallest data-loading seam, and call update-extension with focused patches or edits. Preserve the existing layout, CSS, copy, and interactions. Do not send a reconstructed full content body for a data-only repair; update-extension blocks full-body replacement unless allowFullReplacement: true is explicitly supplied. Use that flag only for a user-requested broad visual rewrite or a complete replacement body supplied by the user. If a focused edit fails, inspect the current body and change the target rather than retrying the same arguments.

Display truncation is cosmetic — do not chase the "missing" tail

A tool result ending in ...[truncated — full result was N chars; only first 50,000 shown] (from the resources read tool or get-extension) means only the DISPLAYED text was capped. The file is intact. run-code's workspaceRead returns the full N chars, and contentFromWorkspaceFile hosts the full file. Never read the same file twice or try to "page the rest" to recover the tail — that is the single biggest source of wasted turns on clone requests. Decide to clone, then go straight to the run-code read+transform+write path in step 5.

Config Shape

{
  "name": "Weekly Metrics",
  "description": "Core product and acquisition metrics",
  // Default grid columns for panels before any section. 1–6, default 2.
  // The grid is always 1 column on small screens and expands at `md:`.
  "columns": 3,
  "filters": [
    {
      "id": "date",
      "type": "date-range",
      "label": "Date Range",
      "default": "30d",
    },
  ],
  "variables": {
    "EVENTS": "`my_project.analytics.events`",
  },
  "panels": [
    // 3 metric cards sit side-by-side at md+ thanks to the dashboard's "columns": 3.
    {
      "id" 
       
       
       
       
          
       
    
    
       
       
       
       
       
          
       
    
    
       
       
       
       
       
          
       
    
    
    
       
       
       
       
       
    
    
       
       
       
       
       
          
       
    
  

Filters And Variables

filters[] defines dashboard-wide controls. Filter values are available in panel SQL through {{var}} interpolation. Date ranges emit {{<id>Start}} and {{<id>End}}.

For dashboard-time-bound first-party SQL, use config.timeScope: "dashboard" and a predicate that consumes the declared filter, such as event_date >= '{{dateStart}}' AND event_date < '{{dateEnd}}'. A {{timeRange}} token must have a matching select filter and SQL branches for its options; date variables must have a matching date-range filter. The server rejects unbound first-party SQL during dashboard validation.

Filter ids must be unique. Two filters with the same id collide on the same URL param, so changing one visibly updates the other in the UI. The dashboard save endpoint rejects duplicates with a 400.

Use type: "date-range" for paired start/end dates. Don't add two type: "date" filters labeled "Start" and "End" — even with distinct ids, that ships the wrong UX. A single date-range filter renders as the "from … to …" pair and exposes both halves to SQL via {{<id>Start}} / {{<id>End}}.

Use variables for shared constants such as table refs or project IDs. Identifier-like variables can be used bare (FROM {{EVENTS}}); string values should be inside SQL string literals ('{{author}}').

Use conditional blocks for optional filters:

{{?country}}AND country = '{{country}}'{{/country}}

Filters auto-apply on change — there is no Apply button. Each filter change writes to the URL and re-runs the affected panels. Other filters are preserved (the URL update is functional, not destructive). If you see a filter "reset" itself when another filter changes, look for a duplicate id first.

Modifying A Dashboard

For existing dashboard edits, default to mutate-dashboard. It gives the agent a typed mutation API without exposing arbitrary JavaScript execution. Pass structured operations for normal agent calls; keep the legacy code form for short layout/config edits only. Large SQL payloads belong in the server-side first-party metric catalog, not in a prompt argument. The server parses only documented dashboard.* method calls, applies the resulting operations in memory, validates the final dashboard config, writes SQL once, syncs collab, and returns compact proof.

Arguments must be JSON-compatible literals, so quote object keys. Variables, imports, loops, functions, templates, network, filesystem, DB access, and calling other actions from the script are not available.

type DashboardMutationApi = {
  dashboard: {
    set(patch: DashboardPatch): void;
    setFilterDefault(
      filterId: string,
      value: string | number | boolean | null,
    ): void;
    panel(id: string): PanelSelection;
    section(id: string): SectionSelection;
    panels(ids: string[]): PanelSelection;
    panelsMatching(filter: PanelFilter): PanelSelection;
    insertPanel(panel: PanelInput): InsertedPanel;
  };
};

type DashboardPatch = {
  name?: string;
  description?: string;
  columns?: number;
  filters?: unknown[];
  variables?: Record<string, string>;
  parentId?: string;
};

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Examples:

dashboard.panels(["dau-over-time", "wau-over-time"]).moveToTop();
dashboard
  .panel("recurring-users-by-template")
  .duplicate("recurring-users-by-template-bar", {
    title: "Recurring Signed-In Users by Template (Bar)",
    chartType: "bar",
  })
  .nextTo("recurring-users-by-template");
dashboard.setFilterDefault("emailFilter", "exclude_builder");
dashboard.panel("top-referrers").setTitle("Top Referrers by Domain");
dashboard.panel("retention").set({
  width: 2,
  config: { description: "Updated definition." },
});
dashboard.panelsMatching({ source: "first-party" }).setWidth(2);
dashboard
  .panelsMatching({ titleIncludes: "Revenue" })
  .setConfigPath("yAxis.format", "currency");
dashboard.panelsMatching({ titleIncludes: "Signed-In" }).moveToTop();
dashboard
  .section("retention-activity-section")
  .append([, ]);
dashboard
  .({
    : ,
    : ,
    : ,
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    : { :  },
    
    : ,
  })
  .();

Native tool call:

{
  "dashboardId": "weekly-metrics",
  "code": "dashboard.panels([\"dau-over-time\",\"wau-over-time\"]).moveToTop();"
}

Use update-dashboard only for new full-config saves, UI full-config saves, or when the user specifically requests low-level JSON-pointer edits.

get-sql-dashboard is compact by default. It returns panel summaries, ids, titles, chart types, sources, layout groups, layout.panelOrder, and layout.firstPanelIds without embedding every panel's full SQL. Use that compact result to find panel ids and verify order. Pass includeConfig: true only when you need full panel SQL/config for a detailed edit.

After a mutation, navigate to the dashboard if the user is elsewhere. The app syncs through the framework's polling/query invalidation path.

Reordering Panels

For simple "move this chart/section" requests, prefer mutate-dashboard with structured operations. The short script form is also supported for one compact move:

{
  "dashboardId": "weekly-metrics",
  "code": "dashboard.panels([\"dau-over-time\",\"wau-over-time\"]).moveToTop();"
}

Do not do index arithmetic with /panels/<index> unless the user specifically asks for a low-level JSON-pointer edit. Use moveBefore, moveAfter, moveToTop, moveToBottom, or moveToIndex against panel ids instead.

get-sql-dashboard returns layout.panelOrder, layout.firstPanelIds, and row/group summaries. Use those fields for orientation and verification instead of re-reading stale screenshots or counting positions from memory.

Existing Dashboard Edits

When the user asks to change existing panels:

1. Read the current dashboard with get-sql-dashboard compact mode unless full SQL/config is required.
2. Call mutate-dashboard once with every change in operations. Use panel ids, not shifted array indexes. The short code form is only for compact layout/config edits; do not stream a large multi-panel SQL script.
3. Verify the returned panelCount, appliedOps, firstPanelIds, and summary. If possible, read the affected panels back and confirm the exact fields changed.

For SQL-only panel edits, use dashboard.panel("id").setSql("..."). If the metric semantics changed, also update the visible definition with setConfigPath("description", "...") or set({ "description": "..." }). If the title, source, chart type, width, or config shape changes together, put them in the same set({...}) call.

First-Party User Metrics

For first-party /track events, be precise about identity:

Metric intent	Identity expression
Account users, DAU, WAU, retention, cohorts	NULLIF(user_id, '') plus NULLIF(user_id, '') IS NOT NULL, but only on events that actually represent the activity being measured
Signed-in visitor activity	event_name = 'session status' AND signed_in = 'true' keyed by COALESCE(NULLIF(user_id, ''), NULLIF(anonymous_id, '')), labeled as signed-in visitors rather than account users
Public traffic, visitors, clip/share viewers	COALESCE(NULLIF(user_id, ''), NULLIF(anonymous_id, ''))

Do not call anonymous visitors "users" in dashboard labels or descriptions. When a user asks for DAU, WAU, retention, repeat users, or account cohorts, exclude logged-out traffic unless they explicitly ask for visitor metrics. If the active/session events do not include account identity, do not substitute signup or identify events and call that DAU/WAU. Either update instrumentation to send account identity on active events, or label the dashboard metric as signed-in visitor activity.

For template/app activity metrics, exclude docs from DAU, WAU, retention, and repeat-user panels. A docs event may carry signed_in = true from shared auth state or tracker context, but docs traffic is not app usage and should not appear as an app/template series. Use a minimum cohort-size threshold for retention rates so one or two identities cannot create misleading 100% or 0% spikes.

Demo Dashboards

ensure-demo-dashboards auto-installs a per-user demo on first app open.

Building Large First-Party Dashboards (compose-dashboard)

For a first-party analytics dashboard, prefer compose-dashboard over hand-authoring a big update-dashboard config. You name the metrics; the SERVER expands each into a full, validated panel (SQL + chart config) from the shipped metric catalog and saves them in ONE atomic call. This avoids the failure mode where the agent must stream a giant multi-panel update-dashboard argument inside the ~40s budget — that big tool-call can't be resumed mid-stream and is all-or-nothing on validation, so the agent thrashes (repeated update-dashboard + tool-search, never landing).

• Never hand-author large first-party configs panel-by-panel. Call compose-dashboard with the metric keys instead.
• Unknown metric keys are skipped and reported in unknownMetrics (not fatal). Each panel's SQL is validated independently — valid panels save, invalid ones are reported in invalidMetrics.
• By default (no overwrite), composing into an existing dashboard APPENDS the new panels and skips ids already present. overwrite: true replaces the whole config.
• Set refreshExisting: true when refreshing a catalog-backed dashboard. Matching metric panels are replaced in place, unrelated panels and layout order are preserved, and the server returns refreshedExistingIds.
• Each metric accepts an optional per-metric window of '30d' | '90d' | 'all' (only affects windowed virality/time metrics) and title / chartType / width overrides. Request 'all' only when the user asks for all-time coverage, and describe it as full available history.
• Returns { saved, dashboardId, panelCount, createdMetrics, refreshedExistingIds, unknownMetrics, invalidMetrics, skippedExistingIds } — report saved: true, panelCount, and any invalid/unknown metrics as proof-of-done.

Available metric keys: total-signups, signups-over-time, signups-by-template, sessions-by-app, sessions-over-time, replay-sessions, replay-chunks-over-time, recent-replay-sessions, signed-in-vs-anon, total-template-clicks, total-demo-clicks, total-cli-copies, template-interest-over-time, clicks-by-template, demo-clicks-by-template, cli-copies-by-template, cli-copies-over-time, pageviews-over-time, top-referrer-domains, referred-signups-30d, viral-signup-share-30d, clip-share-signups-30d, signups-by-referral-source, referred-signups-over-time, top-referrers, share-funnel-30d, viral-participation-rate-90d, viral-coefficient-90d, activated-referrers-90d.

# Build a large first-party dashboard in ONE call (server generates the panels)
pnpm action compose-dashboard --dashboardId first-party-overview --title "First-Party Overview" \
  --metrics '["total-signups","signups-over-time","signups-by-template","sessions-by-app","viral-coefficient-90d","top-referrers","share-funnel-30d"]'

Reliable Bulk Edits

This is the dashboard-specific application of the framework-wide reliable-mutations skill — read that for the general rule (one atomic write, verify end state, report proof-of-done).

Hosted agent runs have a ~40s budget. Many sequential update-dashboard calls (one per panel, plus schema-discovery calls) will blow that budget and leave the dashboard in a partial state — earlier inserts looked like they succeeded (✓), but nothing actually persisted. Avoid this:

• For a large first-party dashboard, use compose-dashboard (see the section above): name the metrics, the server generates the panels in one call. Do not hand-author the big config.
• Batch ALL edits into ONE mutate-dashboard call. Structured operations can move, insert, remove, duplicate, and update many panels. Never loop dashboard edit actions panel-by-panel.
  • To bulk edit existing panels, use selectors: dashboard.panelsMatching({"source":"first-party"}).setWidth(2);
  • To make nested config edits, use setConfigPath("yAxis.format", "percent") instead of resending/clobbering the whole nested object.
• Always verify the returned proof-of-done and report it. mutate-dashboard returns panelCount, appliedOps, panelOrder, firstPanelIds, changedPanelIds, commandLog, and a summary string. Tell the user the resulting panel count instead of assuming success.

# Add or edit several panels in ONE atomic call (never one call per panel)
pnpm action mutate-dashboard --dashboardId weekly-metrics \
  --code 'dashboard.panelsMatching({"source":"first-party"}).setWidth(2);'

Archiving vs deleting

Dashboards have a soft-delete state. The default user-facing destructive action is Archive (recoverable). Hard delete still exists, but lives behind a "Delete permanently" confirm in both the page header and the sidebar dropdown — and in the agent surface, behind the older delete-dashboard action. Archived rows stay in the dashboards table with archived_at set, are hidden from the default sidebar list, and remain accessible by id (so deep links in chat history keep working) until explicitly purged.

# Archive
pnpm action archive-dashboard --id weekly-metrics
# Restore
pnpm action archive-dashboard --id weekly-metrics --archived false

Default the agent to archive when the user says "delete" / "remove" / "get rid of" a dashboard. Reach for hard delete only when the user explicitly says "permanently", "for good", or similar. List queries default to active rows only — use ?archived=1 on /api/sql-dashboards (or the archived: 'all' | 'archived' | 'active' option on listDashboards) to see archived rows.

Sharing

Dashboards are private by default. Use the framework sharing actions:

pnpm action share-resource --resourceType dashboard --resourceId weekly-metrics --principalType org --principalId <org-id> --role viewer
pnpm action set-resource-visibility --resourceType dashboard --resourceId weekly-metrics --visibility org

Writes require editor access; deletes require admin access. Owners always satisfy access checks.

If a dashboard embeds an extension panel (chartType: "extension"), sharing the dashboard does not share the extension. Share the referenced extension to the same audience (share-resource --resourceType extension ...) so all dashboard viewers can see the embedded content; otherwise they get an "extension unavailable" placeholder.

Important Rules

• Never fabricate data or create a dashboard from guessed schema. A panel's SQL must hit a real source; do not present figures you did not actually query.
• Never write dashboard configs into the settings table.
• Never use db-patch as a fallback for dashboard config edits. Use mutate-dashboard for existing edits, or update-dashboard for new/full config saves, and fix the action arguments.
• Never set panel.source to a table name or unsupported backend.
• Use first-party for /track data and query-agent-native-analytics for ad-hoc first-party event questions.
• Use update-dashboard for new dashboard config saves and full config replacements. Use mutate-dashboard for existing dashboard edits.