About this skill
Reference for evaluating machine learning models correctly — picking the right metric for the problem, detecting overfitting, and designing a validation strategy that gives an honest estimate of real-world performance. Use this whenever the user is choosing evaluation metrics for a model, debugging why a model performs well in testing but poorly in production, setting up train/validation/test splits, or reviewing model evaluation results for validity.
Quick start
- Copy the install prompt. Use the green button above this README.
- Send it to your agent. Paste it into your coding agent’s chat and send.
- Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Review, license & listing details
This is an imported listing. We haven’t certified its safety, license, or compatibility. Skills may include executable scripts.
Only SKILL.md was scanned. Supporting files and scripts were not checked. No safety grade has been assigned.
0 potential findings · Scanned 11/4/2026 · Scanner 0.2.1
Content SHA-256: d7baf9205cba1b3300e10c1bf35ecb8e12b80870427caaa0444a8b31d913f8c8
About this listing
- Category
- Engineering
- Reported license
- Not specified
- Listing language (estimated)
- English
- Review status
- Not individually reviewed
- Agent compatibility
- Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.