| { |
| "agent_pool_id": "structured_data_processing", |
| "allowed_edit_scope": "candidate solution and runtime outputs only; packet inputs are immutable", |
| "answer_position": "'RNM'!A1:J20", |
| "answer_sheet": "RNM", |
| "candidate": { |
| "artifact_key": "candidate_solution", |
| "entrypoint": "solve(input_path: str, output_path: str) -> None", |
| "path": "candidate/solution.py", |
| "required_output": "a readable XLSX workbook at output_path", |
| "type": "python_workbook_transformation" |
| }, |
| "instruction": "I need a VBA script that can sum the values in column J of an Excel sheet where the corresponding entries in columns B & C match. Following the summation, I want the script to delete all the rows that contain these duplicate entries in columns B & C, leaving only one row with the sum for each unique pair. I've provided a sample spreadsheet with a 'RNM' sheet that shows the initial data and another sheet that shows the expected result, highlighting the affected items for clarity.", |
| "observable_goal": "Produce one reusable Python workbook transformation that solves every trusted evaluator case.", |
| "public_case": { |
| "case_id": 1, |
| "input_workbook": "public/cases/case_1/input.xlsx" |
| }, |
| "source_id": "250-20", |
| "source_visible_example": { |
| "meaning": "Example supplied by the upstream source; it is not guaranteed to match evaluator cases.", |
| "present": true, |
| "sheet": "Desired outcome" |
| } |
| } |
|
|
