| name | csv-processor |
| description | Read a CSV file from disk, compute per-column min/mean/max for every numeric column, emit the result as JSON. Stdlib-only Python; no pandas, no numpy. Demonstrates the simplest possible "give me a file path, get back structured analysis" skill — a deliberate baseline for any skill that processes tabular data locally without an LLM in the loop. |
| version | 1.0 |
| license | MIT |
| metadata | {"category":"data","tag":["example","csv","statistics","python"]} |
csv-processor
A deterministic, network-free skill — the easiest case. Useful as a control when debugging the agent ↔ skill plumbing: if this fails, the failure is in the runner, not the skill.
Contract
Input (single CLI argument):
python src/main.py /path/to/data.csv
The script reads argv[1] as a filesystem path. CSV must have a header row.
Output (stdout, JSON):
{
"rowCount": 1234,
"columns": {
"price": { "min": 1.23, "mean": 42.0, "max": 999.99, "count": 1234 },
"quantity": { "min": 0, "mean": 7.5, "max": 100, "count": 1230 }
}
}
Only numeric columns appear under columns. count is the number of cells that parsed successfully (numeric); non-numeric / blank cells are skipped.
Errors — written to stderr as {"error": "..."} and exit code 1.
Run locally
cd examples/csv-processor
python src/main.py sample.csv
A sample.csv is bundled so the example runs out of the box.
Adapt this
- Different aggregations — add median, p95, stddev; same shape, more keys per column.
- Streaming — for huge files, replace the in-memory accumulation with a running-mean update; one extra variable per column, same output shape.
- Source other than disk — accept a URL or stdin instead of
argv[1]. The aggregation core doesn't care.
