Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.
How to install ml-model-eval-benchmark?
dvcrn/openclaw-skills-marketplace/ml-model-eval-benchmark
$npx -y skills add dvcrn/openclaw-skills-marketplace --skill ml-model-eval-benchmarkInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/dvcrn/openclaw-skills-marketplace" --skill "dvcrn/openclaw-skills-marketplace/ml-model-eval-benchmark"` 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/dvcrn/openclaw-skills-marketplace" that are relevant to the current task. Run `npx skills add "https://github.com/dvcrn/openclaw-skills-marketplace"` and select the relevant skills, then follow their instructions.
Related
6 picksType
- azure-aiUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR.Jul 2026
- ai-research-exploreRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates.Jul 2026
- ai-research-reproductionRigor Reproduce compatible skill slug for README-first deep learning repository reproduction.Jul 2026
- explore-codeRigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories.Jul 2026
- run-trainRigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup…Jul 2026
- explore-runRigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories.Jul 2026
