inspect-data

name	inspect-data
description	Inspect a data file (JSONL, JSON, Parquet, or HDF5) or a HuggingFace dataset to understand its structure. Usage: /inspect-data <file_path_or_dataset_id> [additional instructions]

Inspect a data file or a HuggingFace dataset to understand its structure. The argument is either a local file path or a HuggingFace dataset identifier, optionally followed by additional instructions.

Parse the argument string: the first token is the path or dataset id; everything after is additional instructions (may be empty).

First — decide whether the target is a local file or a HuggingFace dataset:

• If the argument looks like a dataset id (no file extension and either (a) contains / like allenai/c4, or (b) is a bare name like squad that does not exist as a local path), treat it as a HuggingFace dataset and follow the HuggingFace section below.
• Otherwise, treat it as a local file and follow the per-extension steps.

Preliminary step for local files — check file size: Before inspecting, run ls -l on the file to get its size. If the file is larger than 100 MB, set a large_file flag and pass --large to the inspection script (which reads only a small sample instead of the full file).

The inspection scripts live in scripts/ next to this skill:

• scripts/json_inspector.py
• scripts/parquet_inspector.py
• scripts/hdf5_inspector.py
• scripts/hf_inspector.py

Each script uses uv's inline script metadata to pull its own dependencies. Run them with uv run <script> ... (note: no python — inline metadata is only applied when the script itself is the entry point).

Steps based on file extension:

For .jsonl files:

1. Read only the first line of the file (use the Read tool with limit: 1, or head -n 1 — never load the entire file).
2. Parse it as JSON.
3. Report the keys found in that first entry.

For .json files: Run uv run scripts/json_inspector.py <path> [--large] and show the output to the user. The script prints the top-level type, length/keys, and a preview of the first entries (truncated to 200 chars).

For .parquet files: Run uv run scripts/parquet_inspector.py <path> [--large] and show the output. The script prints row/column counts, per-column dtypes with sample values, the first 3 rows, and the first row as a dict (values truncated to 200 chars). In --large mode it uses pyarrow metadata + the first row group only.

For .h5 / .hdf5 files: Run uv run scripts/hdf5_inspector.py <path> [--large] and show the output. The script walks all groups and datasets, prints shapes/dtypes/attrs, and for each dataset previews the first item (or [:3] / [0] slice in --large mode). String values are truncated to 200 chars.

For HuggingFace datasets: Run uv run scripts/hf_inspector.py <dataset_id> [--config CONFIG] [--split SPLIT] [-n N] and show the output. The script uses load_dataset_builder to read metadata without downloading (description, homepage, license, splits with example counts, feature schema) and then streams the first N examples (default 3) from the chosen split. It never downloads the full dataset, so the --large flag is not used.

Guidance for HuggingFace inspection:

• The --large flag does not apply here.
• If the dataset has multiple configs, run the script first without --config to see the list, then re-run with one of them.
• If the user didn't specify a split, let the script default (it prefers train, then validation, then test).
• If streaming fails (some datasets don't support it), report the error to the user rather than retrying with a full download.

After the inspection, summarize what you found in plain language (structure, field names, example values).

If additional instructions were provided, follow them after completing the inspection above.

Rules:

• Do not modify the data file.
• Do not commit anything.
• Use uv run to execute Python code, not bare python or python3.