training-machine-learning-models
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: training-machine-learning-models
description: | Use when this capability is needed. Train machine learning models with configurable architectu…
category: other
runtime: no special runtime
---
# training-machine-learning-models output preview
## PART A: Task fit
- Use case: | Use when this capability is needed. Train machine learning models with configurable architectures, loss functions, and optimization strategies across classification, regression, and other task types. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / How It Works / When to Use This Skill” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “| Use when this capability is needed. Train machine learning models with configurable architectures, loss functions, and optimization strategies across classification, regression, and other task types. runs entirely locally. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Overview / How It Works / When to Use This Skill” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Overview / How It Works / When to Use This Skill”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: training-machine-learning-models
description: | Use when this capability is needed. Train machine learning models with configurable architectu…
category: other
source: tomevault-io/skills-registry
---
# training-machine-learning-models
## When to use
- | Use when this capability is needed. Train machine learning models with configurable architectures, loss functions, a…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / How It Works / When to Use This Skill” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "training-machine-learning-models" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / How It Works / When to Use This Skill
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Ml Model Trainer
Train machine learning models with configurable architectures, loss functions, and optimization strategies across classification, regression, and other task types.
Overview
This skill empowers Claude to automatically train and evaluate machine learning models. It streamlines the model development process by handling data analysis, model selection, training, and evaluation, ultimately providing a persisted model artifact.
How It Works
- Data Analysis and Preparation: The skill analyzes the provided dataset and identifies the target variable, determining the appropriate model type (classification, regression, etc.).
- Model Selection and Training: Based on the data analysis, the skill selects a suitable machine learning model and configures the training parameters. It then trains the model using cross-validation techniques.
- Performance Evaluation and Persistence: After training, the skill generates performance metrics to evaluate the model's effectiveness. Finally, it saves the trained model artifact for future use.
When to Use This Skill
This skill activates when you need to:
- Train a machine learning model on a given dataset.
- Evaluate the performance of a machine learning model.
- Automate the machine learning model training process.
Examples
Example 1: Training a Classification Model
User request: "Train a classification model on this dataset of customer churn data."
The skill will:
- Analyze the customer churn data, identify the churn status as the target variable, and determine that a classification model is appropriate.
- Select a suitable classification algorithm (e.g., Logistic Regression, Random Forest), train the model using cross-validation, and generate performance metrics such as accuracy, precision, and recall.
Example 2: Training a Regression Model
User request: "Train a regression model to predict house prices based on features like size, location, and number of bedrooms."
The skill will:
- Analyze the house price data, identify the price as the target variable, and determine that a regression model is appropriate.
- Select a suitable regression algorithm (e.g., Linear Regression, Support Vector Regression), train the model using cross-validation, and generate performance metrics such as Mean Squared Error (MSE) and R-squared.
Best Practices
- Data Quality: Ensure the dataset is clean and properly formatted before training the model.
- Feature Engineering: Consider feature engineering techniques to improve model performance.
- Hyperparameter Tuning: Experiment with different hyperparameter settings to optimize model performance.
Integration
This skill can be used in conjunction with other data analysis and manipulation tools to prepare data for training. It can also integrate with model deployment tools to deploy the trained model to production.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
Source: ComeOnOliver/skillshub — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review