Individual skill
LQF_Machine_Learning_Expert_Guide
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.
bundled/skills/LQF_Machine_Learning_Expert_Guide/SKILL.md
Source commit bd10381417cbd890ed6f08a18bb18fb5aaf0f741
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- Matches the observed repository HEAD
- Yes
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- Yes
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- No
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- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 3,810
- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- ai-workflow Repository topic
- automation Repository topic
- codex Repository topic
- developer-tools Repository topic
- skills Repository topic
- ai-skills Repository topic
- claude-code Repository topic
- llm Repository topic
- vibe-coding Repository topic
- engineering Derived category
- education Derived category
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Read excerpt
# 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