# LQF Machine Learning Expert Guide

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with...

- Repository: https://github.com/foryourhealth111-pixel/Vibe-Skills (3789 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill LQF_Machine_Learning_Expert_Guide -g -a claude-code -y`
Codex: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill LQF_Machine_Learning_Expert_Guide -g -a codex -y`
Cursor: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill LQF_Machine_Learning_Expert_Guide -g -a cursor -y`
Gemini CLI: `npx skills add foryourhealth111-pixel/Vibe-Skills --skill LQF_Machine_Learning_Expert_Guide -g -a gemini-cli -y`

## Description

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# LQF Machine Learning Expert Guide

## When to Use This Skill

Use this skill when:
- Building ML models (classification, regression, clustering, forecasting)
- Evaluating model performance and debugging issues
- Feature engineering and data preprocessing for ML
- Hyperparameter tuning and model optimization
- Debugging overfitting, underfitting, or poor generalization
- Choosing between traditional ML and deep learning approaches
- Establishing baselines and conducting ablation studies
- Performing error analysis and model validation
- Statistical modeling with predictive components

## Not For / Boundaries

**Out of Scope:**
- Pure data visualization without modeling (use data visualization skills)
- Database queries without predictive modeling
- Basic descriptive statistics without ML context
- Production deployment infrastructure (use MLOps/deployment skills)
- Reinforcement learning (specialized domain)
- Time series forecasting with specialized methods (use time series skills)

**Required Inputs - Ask User If Missing:**
1. What is the problem type? (classification, regression, clustering, etc.)
2. What does your data look like? (size, number of features, target variable distribution)
3. Have you established a baseline yet? (dummy predictor, simple heuristic)

## Critical Discussion Protocol

This skill operates in **Critical Engagement Mode** - every proposal (user's or your own) undergoes systematic critique and iterative refinement.

### Core Principles

1. **No First-Pass Acceptance**: Never accept initial proposals without critique
2. **Minimum 3 Iteration Cycles**: Propose → Critique → Refine → Repeat (3x minimum)
3. **Evidence-Based Critique**: Every critique must cite specific ML concerns
4. **Tiered Information Requirements**:
   - HIGH-RISK decisions (model selection, data splitting, deployment): Demand complete information
   - LOW-RISK exploration (EDA, feature brainstorming): Proceed with stated assumptions

### Critique Intensity Levels

The rest: https://github.com/foryourhealth111-pixel/Vibe-Skills/blob/HEAD/bundled/skills/LQF_Machine_Learning_Expert_Guide/SKILL.md