Nlq dashboard orchestrator

NLQ Dashboard Orchestrator

This skill is the front door of the toolkit. It accepts free-form natural language (e.g., "Build me a sales performance dashboard from my Excel file" or "Show monthly revenue and top customers using our Snowflake warehouse") and drives the entire pipeline to a finished PBIP dashboard.

When to Use This Skill

• The user describes a dashboard, report, or set of visuals in natural language
• No structured YAML / SQL / data-model.json is provided up front
• The agent needs to coordinate data-source-connector, query-to-pbip, theme-branding, and bi-dash-creator
• An explicit, gated, confirmation-driven workflow is required

Pipeline Stages

┌──────────────────────────────────────────────────────────────────┐
│ 0. Capture NLQ intent                                            │
│ 1. data-source-connector → data-model.json                       │
│ 2. GATE A: confirm data model readiness with the user            │
│ 3. NLQ Q&A loop: visuals, fields, filters, layout                │
│ 4. GATE B: confirm scaffold readiness with the user              │
│ 5. For each visual: run query-to-pbip pipeline                   │
│ 6. theme-branding → apply theme                                  │
│ 7. bi-dash-creator → compose multi-visual dashboard              │
│ 8. Deliver zipped PBIP + summary                                 │
└──────────────────────────────────────────────────────────────────┘

Stage 0: Capture NLQ Intent

Extract the following from the user's free-form request:

• Dashboard theme / topic (e.g., "sales performance", "delivery operations")
• Data source hint (e.g., "Excel file", "our Databricks warehouse", "the orders database")
• Implicit visual count (e.g., "trend, top 5, total" suggests 3 visuals)
• Time scope (e.g., "last 12 months", "year-to-date", "2024")
• Audience cues (executive, operational, analytical)

If the data source is not mentioned, ask: "Where does the data live? (database, cloud warehouse, Excel/CSV file, API, etc.)"

Stage 1: Invoke data-source-connector

Delegate to the data-source-connector skill with the source hint. Expected output:

• data-model.json describing tables, columns, types, primary/foreign keys, sample rows
• A list of open questions the connector could not resolve automatically

If the connector returns open questions, surface them verbatim to the user. Do not proceed to Gate A until all open questions are answered.

Stage 2: GATE A — Data Model Readiness

This gate is mandatory. Before any modeling work begins, summarize the discovered (or user-described) data model and ask for confirmation.

Present a structured summary:

Data Model Summary
──────────────────
Source:         <connection or file path>
Tables:         <N> tables discovered
  • fact_sales (12 cols, ~50k rows) — grain: order line
  • dim_customer (8 cols)
  • dim_date (15 cols)
Relationships:  3 inferred
  • fact_sales.customer_key → dim_customer.customer_key
  • fact_sales.order_date_key → dim_date.date_key
  • fact_sales.product_key → dim_product.product_key
Date dimension: dim_date (date, year, month, quarter columns present)
Open questions:
  1. Is `dim_product` needed for this dashboard?
  2. Should `order_date_key` or `delivered_date_key` be the default date role?

Then ask explicitly: "Does this data model look right? Should I add, remove, or rename anything before continuing?"

Blocking rule: do NOT progress to Stage 3 until the user confirms the data model. If the user reports missing tables, ambiguous grain, or unclear relationships, loop back to data-source-connector for re-introspection or accept user-provided corrections to data-model.json.

Stage 3: NLQ Q&A Loop

For each visual implied by the request, iteratively clarify:

Question Type	Example
Visual count	"How many visuals do you want on the dashboard? (default: 4 — trend, top-N, KPI, breakdown)"
Measure	"Which measure should the KPI card show — total revenue, total orders, or both?"
Dimension	"Should the trend be by month or by day?"
Filter	"Any default filters (region, year, status)?"
Layout	"Standard 2x2 grid, or do you want a hero KPI row on top?"
Theme	"Corporate, modern, minimal, or dark theme?"

Track all answers in a Dashboard Plan internal structure:

{
  "dashboardName": "SalesPerformance",
  "theme": "corporate",
  "layout": "2x2-grid",
  "visuals": [
    { "id": "v1", "intent": "total revenue KPI", "type": "cardVisual", "measure": "Total Revenue", "filters": [] },
    { "id": "v2", "intent": "monthly revenue trend", "type": "lineChart", "category": "dim_date.month", "y": "Total Revenue" },
    { "id": "v3", "intent": "top 10 customers", "type": "clusteredBarChart", "category": "dim_customer.name", "y": "Total Revenue", "topN": 10 },
    { "id": "v4", "intent": "revenue by region", "type": "filledMap", "category": "dim_customer.region", "size": "Total Revenue" }
  ]
}

Use visual-selector rules to suggest defaults when the user is unsure.

Stage 4: GATE B — Scaffold Readiness

This gate is mandatory. Before any file generation, present the full Dashboard Plan and ask for explicit confirmation.

Present the plan:

Dashboard Plan
──────────────
Name:    SalesPerformance
Theme:   corporate
Layout:  2x2 grid on a single 1280x720 page

Visual 1 (top-left)    — Card: Total Revenue
Visual 2 (top-right)   — Line Chart: Total Revenue by Month
Visual 3 (bottom-left) — Clustered Bar: Top 10 Customers by Revenue
Visual 4 (bottom-right)— Filled Map: Revenue by Region

All visuals share the dim_date filter context (default: last 12 months).

Then ask verbatim: "Are you done with clarifications and ready to scaffold all visuals into the final dashboard? (yes / no — let me know if anything should change)"

Blocking rule: do NOT progress to Stage 5 until the user answers yes (or equivalent affirmative). If the user wants changes, loop back to Stage 3.

Stage 5: Run query-to-pbip per Visual

For each visual in the confirmed Dashboard Plan:

1. Pass the visual's spec + data-model.json to query-to-pbip
2. query-to-pbip runs its four stages (semantic-mapper → visual-selector → visual-generator → project-packager)
3. Output lands in generated-reports/<VisualName>/

Use --repo-root when scaffolding so the SemanticModel is shared across visuals (avoids duplicating TMDL per visual).

Stage 6: Apply Theme

Delegate to theme-branding:

• Theme name from the Dashboard Plan (corporate / modern / minimal / dark, or a custom theme)
• Copies the theme JSON into <ProjectName>.Report/StaticResources/SharedResources/BaseThemes/
• Updates report.json themeCollection accordingly

Stage 7: Compose Final Dashboard

Delegate to bi-dash-creator with the list of generated report names. This skill:

• Validates semantic-model consistency across reports
• Filters excluded visual types (cards/slicers/kpis stay; the rest go on the dashboard)
• Assigns 2x2 grid positions (or honors a custom layout from Stage 3)
• Outputs generated-dashboards/<DashboardName>Dash/

Stage 8: Deliver

Provide the user with:

• Path to the zipped PBIP
• Summary of generated artifacts (tables, measures, visuals, theme)
• Open-in-Power-BI-Desktop instructions

Error Handling

Failure	Recovery
data-source-connector cannot reach the source	Ask user for credentials / file path; do not proceed past Stage 1
User cannot confirm data model (Gate A)	Loop back to Stage 1 with corrections
Visual spec references a field not in data-model.json	Ask user to map to an existing field or add the field via Stage 1
User declines at Gate B	Loop back to Stage 3
query-to-pbip fails on a visual	Report which visual failed; ask user whether to skip, fix, or abort
Theme application fails	Fall back to default CY25SU11 theme and warn the user

Resources

• references/clarification-prompts.md — Standard question bank for the NLQ Q&A loop
• references/dashboard-plan-schema.md — JSON schema for the internal Dashboard Plan
• references/example-flows.md — Worked end-to-end examples (Excel, Databricks, SQL Server)

Cross-Skill References

Stage	Skill
Stage 1	data-source-connector
Stages 5	query-to-pbip (which internally uses semantic-mapper, visual-selector, visual-generator, project-packager)
Stage 6	theme-branding
Stage 7	bi-dash-creator