Run a quality gate over the whole Halden pack
Your goal
Find the 17 planted defects in 10 files in dirty/ and flag every affected line or missing record in findings.jsonl.
- 17 defects
- 1.3 MB of data
- FromLineage: proving where an answer came from
- Halden Freight
Pro lab
This lab is in Pro.
The brief, the hints, and grading against the kit’s scorer are in Pro. Stage 1’s lab is free.
- Every stage and specialisation, in full
- Every lab and every role project
- Halden weeks 2–10, on your record
- Every drill, live in the playground
- All solutions, plus the server judge
- Lab Assistant and every Teach with AI
Defects to find
D01Duplicate shipment rows from overlapping nightly extracts
81 lines to find
D02Mixed date formats in portal events (DD/MM/YYYY and MM/DD/YYYY)
9,272 lines to find
D03Local times without a time zone
9,320 lines to find
D04Leading zeros stripped from SAP vendor IDs
4 lines to find
D05Carrier name aliases
6,976 lines to find
D06Port codes with case and whitespace noise
310 lines to find
D07Missing currency on invoice amounts
247 lines to find
D08Portuguese number format in extracted amounts
394 lines to find
D09Weights in pounds
167 lines to find
D10Mojibake in names and text
17 lines to find
D11A merged title row and notes rows in the spreadsheet
4 lines to find
D12Late-arriving and out-of-order events
244 lines to find
D13Exceptions that point at shipments not yet in the extract
6 lines to find
D14Free text in numeric fields
213 lines to find
D15HS codes with and without the dot
2,749 lines to find
D18Duplicate events in the desk's event log
765 lines to find
D19Desk-days missing from the event log
3 missing records to name
How it’s graded
Precision ≥ 0.95 and recall ≥ 0.95, for each defect.
The bar: the bar the Kelverne capstone sets for every defect.
Halden · Your client in the residency: Halden Freight, a simulated freight company you work with for ten weeks. Its labs run on the same company's data. Your client: Halden
Here the client data (the estate: a fictional client’s files, with documented problems planted in them) is already generated, with the kit’s seed: your code reads the files under dirty/. Run tests and Submit both grade your findings with the kit’s score.py, scoped as the task’s command scopes it; only Submit goes on your record. The written parts of the task aren’t graded here; they’re for your notes or your reviewer.
Run tests calls find() and grades what it returns. Submit does the same and saves the result.
⌘/Ctrl+Enter runs the tests, add Shift to submit. First run downloads 1.3 MB of data.