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Dataset versioning skill using DVC for tracking data changes, managing data pipelines, and ensuring reproducibility.
Passed 19 of 21 test cases with the skill, and 4 of 21 without it.
and 23 more in the full report
17 fixed3 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
Execute a data transformation stage named `preprocess` that takes `data/raw.csv` and outputs `data/features.csv`. Return the JSON object matching the execution output schema.
| Checked | Without skill | With skill |
|---|---|---|
| Response payload sets action property to run. | Missed | Passed |
| Response payload includes status property. | Passed | Passed |
The instructions your model reads when the skill loads.
Dataset versioning skill using DVC (Data Version Control) for tracking data changes, managing data pipelines, and ensuring reproducibility in ML workflows.
{
"type": "object",
"required": ["action"],
"properties": {
"action": {
"type": "string",
"enum": ["init", "add", "push", "pull", "diff", "checkout", "run", "repro"],
"description": "DVC action to perform"
},
test cases passed, vs 4 / 21 without
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