# Investigate Dataset

> Investigate Dataset is an Agent Skill for documents published in the UKGovernmentBEIS/inspect_evals repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Investigate datasets from HuggingFace, CSV, or JSON files to understand their structure, fields, and data quality. Trigger whenever you need to explore or inspect a dataset yourself without using pre-written scripts.

- Skill name: `investigate-dataset`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/UKGovernmentBEIS/inspect_evals/blob/HEAD/.claude/skills/investigate-dataset/SKILL.md
- Repository: UKGovernmentBEIS/inspect_evals (584 GitHub stars when collected)

## SKILL.md

# Investigate Dataset

This workflow helps you explore and understand datasets used in evaluations. It covers HuggingFace datasets, CSV files, and JSON/JSONL files.

## Key Concepts

For detailed information on Inspect's dataset types (`datasets.Dataset` vs `inspect_ai.dataset.Dataset`), the `hf_dataset()` pipeline, caching behaviour, and test utilities, see `references/inspect-dataset-patterns.md`.

### Common Patterns in Evals

Evals typically define:

- `DATASET_PATH`: HuggingFace repo path (e.g., `"qiaojin/PubMedQA"`)
- `DATASET_REVISION`: Optional git revision/tag for reproducibility
- `record_to_sample()`: Function converting raw records to `Sample` objects

## Prerequisites

- Access to the evaluation code to find dataset configuration
- Python environment with `datasets`, `pandas`, and `inspect_ai` installed

## Steps

### 1. Identify the Dataset Source

Look for these patterns in the evaluation code:

```python
# HuggingFace dataset
DATASET_PATH = "org/dataset-name"
DATASET_REVISION = "v1.0"  # optional
hf_dataset(path=DATASET_PATH, name="subset", split="train", ...)

# CSV dataset
csv_dataset("path/to/file.csv", ...)
load_csv_dataset("https://example.com/file.csv", eval_name="myeval", ...)

# JSON/JSONL dataset
json_dataset("path/to/file.json", ...)
load_json_dataset("https://example.com/file.jsonl", eval_name="myeval", ...)
```

### 2. Load the Raw Dataset

For investigation, load the raw data directly (not through Inspect's `sample_fields` transformation). Use standard `datasets.load_dataset()` for HuggingFace, `pd.read_csv()` for CSV, or `pd.read_json()` for JSON/JSONL. For gated datasets, ensure `HF_TOKEN` is set or run `huggingface-cli login`.

### 3. Explore Structure and Quality

Use standard pandas/datasets methods to explore:

- **Schema**: `ds.features` (HF) or `df.dtypes` (pandas)
- **Shape**: `len(ds)`, `ds.column_names` (HF) or `df.info()`, `df.columns` (pandas)
- **Sample data**: `ds[:3]` (HF) or `df.head()` (pandas)
- **Missing values**: Check for `None`, empty strings, empty lists
- **Duplicates**: Check ID uniqueness if an ID field exists
- **Value distributions**: `value_counts()` for categorical columns, length stats for text fields

For converting an Inspect `Dataset` (which has no `.to_pandas()`) to a DataFrame, see `references/inspect-dataset-patterns.md`.

### 4. Understand the Sample Conversion

Look at the `record_to_sample` function to understand how raw data maps to Inspect samples. Key questions:

- Which fields become `input`? Are they combined/formatted?
- What is the `target` format? (letter, text, JSON, etc.)
- Are there `choices` for multiple choice?
- What goes into `metadata`?
- Are any records filtered out?

### 5. Test the Inspect Loading Pipeline

See `references/inspect-dataset-patterns.md` for the pattern to load through Inspect's `hf_dataset()` and verify sample conversion works correctly.

## Quick Reference Commands

```bash
# View HF dataset info without downloading
uv run python -c "from datasets import load_dataset_builder; b = load_dataset_builder('org/name'); print(b.info)"

# List available configs/subsets
uv run python -c "from datasets import get_dataset_config_names; print(get_dataset_config_names('org/name'))"

# List available splits
uv run python -c "from datasets import load_dataset; print(load_dataset('org/name', split=None).keys())"
```

## Caching and Troubleshooting

For cache locations (HuggingFace native, Inspect AI, Inspect Evals), force re-download commands, and test utilities, see `references/inspect-dataset-patterns.md`.

- **Gated dataset**: Run `huggingface-cli login` or set `HF_TOKEN`
- **Rate limited**: The `hf_dataset` wrapper in `inspect_evals.utils.huggingface` has built-in retry with backoff
- **Large dataset**: Use `streaming=True` or `split="train[:1000]"` for sampling
- **Missing revision**: Check the dataset's "Files and versions" tab on HuggingFace

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Source: https://www.openparable.com/skill/documents/investigate-dataset (OpenParable). Cite the canonical URL above.