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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
1. HUD API key: https://www.hud.so/project/api-keys
2. Anthropic API key: https://console.anthropic.com/settings/keys
### Installation & Run
```bash
# 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
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