Train a YOLO model on labeled dataset using ultralytics. Splits data into train/val, generates dataset.yaml, and produces trained weights. Use after labeling and augmenting.
How to install train?
qtzx06/yolodex/train
$npx -y skills add qtzx06/yolodex --skill trainInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/qtzx06/yolodex" --skill "qtzx06/yolodex/train"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/qtzx06/yolodex" that are relevant to the current task. Run `npx skills add "https://github.com/qtzx06/yolodex"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- gpt-image-2Create or edit images with Pilio GPT Image 2 through the unified Pilio developer API.Jul 2026
- spec-driven-implementationDrive a spec-first workflow for substantial features by writing PRODUCT.md before implementation, writing TECH.md when warranted, and keeping both specs…Jul 2026
- review-prReview a pull request diff and write structured feedback to review.json for the workflow to publish.Jul 2026
- write-product-specWrite a PRODUCT.md spec for a significant user-facing feature in Warp, focused on detailed behavior and validation.Jul 2026
- write-tech-specWrite a TECH.md spec for a significant Warp feature after researching the current codebase and implementation constraints.Jul 2026
- fix-errorsFix compilation errors, linting issues, and test failures in the warp Rust codebase. Covers presubmit checks, WASM-specific errors, and running specific tests.Jul 2026
