cuML Machine Learning Skill
Skill যাচাই করাUse for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
This skill leverages NVIDIA cuML for high-performance machine learning on tabular data. It supports a wide range of tasks including classification, regression, clustering, dimensionality reduction, and preprocessing, with examples provided for each. The skill automatically detects and utilizes GPU acceleration when available, falling back to CPU-based scikit-learn if not. It requires input data to be in float32 or float64 format and categorical features to be encoded.
Documentation
- info:Configuration & parameter referenceWhile parameters for models are implicitly referenced by their use in examples, explicit documentation for all parameters, including defaults, is missing. The initialization code provides a fallback mechanism but doesn't explicitly document precedence.
Versioning
- warning:Release ManagementThere is no version information (e.g., from SKILL.md frontmatter, package.json, or CHANGELOG) to indicate a specific release version of this skill.
Code Execution
- warning:ValidationWhile the skill attempts to cast input data to float32/float64, explicit schema validation or sanitization for file paths or other inputs is not evident.
- info:LoggingThe initialization code prints messages to stderr indicating GPU availability or fallback, but there is no structured audit log for actions taken.
ইনস্টলেশন
npx skills add langchain-ai/deepagentsVercel skills CLI (skills.sh)-কে npx-এর মাধ্যমে চালায় — স্থানীয়ভাবে Node.js এবং অন্তত একটি skills-সামঞ্জস্যপূর্ণ এজেন্ট (Claude Code, Cursor, Codex, …) ইনস্টল করা থাকতে হবে। ধরে নেওয়া হয় রিপোজিটরিটি agentskills.io ফরম্যাট অনুসরণ করে।
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