Datasets:

hud-evals
/

SheetBench-50

dataset_info:
  features:
    - name: prompt
      dtype: string
    - name: mcp_config
      dtype: string
    - name: id
      dtype: string
    - name: metadata
      dtype: string
    - name: setup_tool
      dtype: string
    - name: evaluate_tool
      dtype: string
    - name: system_prompt
      dtype: string
    - name: agent_config
      dtype: string
  splits:
    - name: train
      num_bytes: 130705
      num_examples: 50
  download_size: 45847
  dataset_size: 130705
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Task Categories

1. Data Preparation and Hygiene (29 tasks)

De-duplication, type normalization, time parsing, joins/FX conversions, pivot tables

2. Derivations & Extraction (16 tasks)

Correlations, z-scores, grouping logic, compliance filters (e.g., 1099)

3. Modeling & Forecasts (5 tasks)

Revenue/breakeven projections, amortization schedules, depreciation calculations, scenario tables

Example Task

For the ticker that has the greatest correlation between volume and next day price change % 
find the day with the greatest volume and the next days price change %

- put the ticker in ANSWER A1
- put the volume in ANSWER B1
- put the next day price change in ANSWER C1

NOTE:
- use CORREL to determine correlation for each ticker group
- be sure to first sort the date by ticker z to a and then date ascending before calculating nextdaypricechange %

System prompt

All solutions should be put in the sheet called ‘ANSWER’.
In the answer sheet, all dates should use the American standard format MM/DD/YYYY with no leading zero.
All numbers should use the format and decimal place precision given in the input sheets (e.g., with or without a thousands separator should depend on the inputs), unless specified otherwise.

Quick Start

Prerequisites

HUD API key: https://www.hud.so/project/api-keys
Anthropic API key: https://console.anthropic.com/settings/keys

Installation & Run

# Install HUD SDK
uv tool install hud-python

# Configure API keys
hud set HUD_API_KEY=... ANTHROPIC_API_KEY=...

# Run evaluation with Claude
hud eval hud-evals/SheetBench-50 claude

# View full dataset
hud get hud-evals/SheetBench-50

Key Features

Production-grade: Tasks validated by finance professionals from PwC, Cisco, Charles Schwab, Fannie Mae
Blind validation: Each task has single reproducible solution with expert consensus
Full telemetry: Records actions, reasoning traces, and screenshots
Tool dexterity: Tests real spreadsheet operations (pivots, formatting, formulas)

Results

View example scorecard: https://www.hud.so/leaderboards/hud-evals/SheetBench-50?scorecard=19c2f4b7-ea8a-4c2b-866f-20ae57976d13
Replay trajectories: https://www.hud.so/jobs/7c06c24e-22c7-4c9a-a667-1de4bb05b080

Contact

For enterprise evaluations or custom benchmarks: founders@hud.so