SQL To BI Builder

Overview

Use this skill to transform sql.md query collections into a service-based BI prototype. This skill must generate both backend and frontend services from SQL-derived artifacts.

Workflow

1. Parse markdown SQL blocks into a normalized query catalog.
2. Infer query semantics (metrics, dimensions, time columns, grain hints).
3. Extract P0 filter candidates from SQL DSL (WHERE predicates) into structured filter metadata (dsl_ast first, regex fallback).
4. Recommend chart types from inferred semantics.
5. Build a dashboard specification with layout coordinates.
6. Generate a UI scaffold that renders the dashboard structure.
7. Generate service bundle (services/backend + services/frontend) that depends on generated SQL artifacts.

Input Contract

Expect one markdown file with one or more SQL fenced blocks. Use this pattern for best results:

# Sales Dashboard

## card: Daily GMV
- id: daily_gmv
- datasource: mysql_prod
- refresh: 5m
- chart: auto
- filters: date, region

```sql
SELECT DATE(pay_time) AS dt, SUM(amount) AS gmv
FROM orders
WHERE pay_status = 'paid'
GROUP BY 1
ORDER BY 1;

Rules:
- Keep one logical query per SQL fenced block.
- Provide stable `id` metadata when possible.
- Keep aliases explicit (`AS alias`) to improve semantic inference.

## Python Environment Setup (Required)
Run from the skill folder.

1. Ensure `python3.11` is installed and available in `PATH`.
   If missing, follow `references/install_python311.md`.
2. Create virtual environment:

```bash
bash scripts/setup_venv.sh

3. Activate and verify:

source .venv/bin/activate
python --version

Expected version: Python 3.11.x.

Use --with-dev when dev dependencies are needed:

bash scripts/setup_venv.sh --with-dev

Run Commands

After activating .venv, run pipeline and service generation:

python scripts/run_pipeline.py \
  --input /abs/path/sql.md \
  --out /abs/path/out \
  --with-services

Run each step separately when debugging:

python scripts/parse_sql_md.py --input /abs/path/sql.md --output /abs/path/out/query_catalog.json
python scripts/infer_semantics.py --input /abs/path/out/query_catalog.json --output /abs/path/out/semantic_catalog.json
python scripts/recommend_chart.py --input /abs/path/out/semantic_catalog.json --output /abs/path/out/chart_plan.json
python scripts/build_dashboard_spec.py --queries /abs/path/out/query_catalog.json --semantics /abs/path/out/semantic_catalog.json --charts /abs/path/out/chart_plan.json --output /abs/path/out/dashboard.json
python scripts/generate_ui_scaffold.py --dashboard /abs/path/out/dashboard.json --out /abs/path/out/ui
python scripts/generate_service_bundle.py --artifacts /abs/path/out --output /abs/path/out/services

Start generated services:

bash /abs/path/out/services/start_backend.sh
bash /abs/path/out/services/start_frontend.sh

Runtime And Version Control

• Use Python 3.11.x only.
• Keep .python-version at 3.11.
• Keep pyproject.toml requires-python = ">=3.11,<3.12".
• Install dev dependency before running upstream validator: pip install -r requirements-dev.txt.
• Commit changes by scope: parser, semantics, chart rules, layout rules, scaffold.
• Tag stable milestones using semantic version tags such as v0.1.0, v0.2.0.

Outputs

• query_catalog.json: Parsed query units and metadata.
• semantic_catalog.json: Field roles, grain hints, and dsl_filters extracted from SQL conditions. dsl_filters includes value_type and value_format, with date support for: yyyy-mm-dd, yyyy/mm/dd, yyyymmdd, yyyy-mm-dd hh:mm:ss, ISO-8601, yyyymmdd_int, unix second/ms integers.
• chart_plan.json: Recommended chart type per query.
• dashboard.json: Final dashboard definition for rendering, including page-level global_filters.
• ui/: Static UI scaffold (index.html, app.js, style.css).
• services/backend: FastAPI backend service using generated artifacts.
• services/frontend: Frontend service consuming backend API.
• services/start_backend.sh and services/start_frontend.sh: service start scripts.

UI Upgrade Notes (2026-03)

When using repo-level service UI (services/frontend), the upgraded experience includes:

• KPI summary strip (click-to-focus widgets)
• Layout switch (Classic / Focus)
• New Midnight Ops theme preset
• stronger visual hierarchy for demos

Heuristic References

Load only the file needed for the current issue:

• SQL parsing and naming constraints: references/sql_style.md
• Chart mapping rules: references/chart_rules.md
• BI layout and widget sizing: references/layout_rules.md
• Python 3.11 installation and venv setup: references/install_python311.md

Limits And Escalation

Treat current scripts as heuristic MVP. Escalate for manual review when SQL includes nested CTE chains, window-heavy ranking logic, or unions with incompatible column semantics. Fallback to table visualization when chart confidence is low.