Runs read-only SQL against analytics.duckdb (default) or lakehouse.duckdb
on the prod server over SSH. SQL is base64-encoded to avoid shell escaping.
Supports TSV (default) and JSON output. Blocks mutation keywords.
For lakehouse, works around the DuckDB catalog naming issue (SQLMesh views
reference "local" but the file creates catalog "lakehouse") by attaching
the file as the "local" catalog.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Script now lives globally as a uv inline-dependency script.
Removes per-project scripts/ci.py and the msgspec dev dependency.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Copies ci.py from beanflows (same script, shared across projects).
Adds msgspec dev dependency required by the script.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The lakehouse.duckdb file uses catalog "lakehouse" not "local", causing
SQLMesh logical views to break. Script now auto-detects the catalog via
USE and falls back to physical tables when views fail.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add scripts/check_pipeline.py: read-only diagnostic for pricing pipeline
row counts, date range analysis, HAVING filter impact, join coverage
- Add description field to all 12 workflows in workflows.toml
- Parse and display descriptions on extraction status cards
- Show spinner + "Running" state with blue-tinted card border
- Display start time with "running..." text for active extractions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- test_stripe_sandbox.py: API-only validation of all 17 products (67 tests)
- stripe_e2e_setup.py: webhook endpoint registration via ngrok
- stripe_e2e_test.py: live webhook tests with real DB verification (67 tests)
- stripe_e2e_checkout_test.py: checkout webhook tests for credit packs,
sticky boosts, and business plan PDF purchases (40 tests)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- eurostat.py: add nama_10r_2hhinc dataset config; append filter params to
request URL so server pre-filters the large cube before download
- stg_regional_income.sql: new staging model — reads nama_10r_2hhinc.json.gz,
filters to NUTS-1 codes (3-char), normalises EL→GR / UK→GB
- dim_locations.sql: add admin1_to_nuts1 VALUES CTE (16 German Bundesländer)
+ regional_income CTE; final SELECT uses COALESCE(regional, country) income
- init_landing_seeds.py: add empty seed for nama_10r_2hhinc.json.gz
Munich/Bayern now scores ~29K PPS vs Chemnitz/Sachsen ~19K PPS instead of
both inheriting the same national average (~25.5K PPS).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Creates minimal .jsonl.gz and .json.gz seed files so all SQLMesh staging
models can compile and run before real extraction data arrives.
Each seed has a single null record filtered by the staging model's WHERE
clause (tenant_id IS NOT NULL, geoname_id IS NOT NULL, type IS NOT NULL, etc).
Covers both formats (JSONL + blob) for the UNION ALL transition CTEs:
playtomic/1970/01/: tenants.{jsonl,json}.gz, availability seeds (morning + recheck)
geonames/1970/01/: cities_global.{jsonl,json}.gz
overpass_tennis/1970/01/: courts.{jsonl,json}.gz
overpass/1970/01/: courts.json.gz (padel, unchanged format)
eurostat/1970/01/: urb_cpop1.json.gz, ilc_di03.json.gz
eurostat_city_labels/1970/01/: cities_codelist.json.gz
ons_uk/1970/01/: lad_population.json.gz
census_usa/1970/01/: acs5_places.json.gz
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
