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auditing-deep-learning-overfit

Audit deep-learning training runs for overfit and generalization failures.

2|Updated May 23, 2026
Search Tags:#deep-learning#regularization#early-stopping#overfit#validation-loss#distribution-shift#label-noise
One-click install
npx skills add https://github.com/rocklambros/rcs --skill auditing-deep-learning-overfit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auditing-deep-learning-overfit
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/auditing-deep-learning-overfit
Command: npx skills add https://github.com/rocklambros/rcs --skill auditing-deep-learning-overfit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you determine whether a deep-learning model is truly overfitting, or whether the symptoms are actually caused by distribution shift, label noise, underfitting, or a simple training plateau.

Core Features & Use Cases

  • Trajectory-based diagnosis: Compares training and validation curves across epochs instead of guessing from a single checkpoint.
  • Guardrailed triage: Checks for train-validation distribution mismatches, mislabeled validation examples, and weight-norm trends before recommending regularization.
  • Actionable remediation: Prioritizes early stopping, augmentation, dropout, weight decay, capacity reduction, and more data in the right order.
  • Best for: CNNs, RNNs, LSTMs, Transformers, and dense MLPs when validation performance degrades after initially improving.

Quick Start

Ask the skill to audit your deep-learning training run using the train and validation histories, optional weight norms, and any dataset summaries, then return the diagnosis and the recommended next steps in priority order.

Frequently Asked Questions about auditing-deep-learning-overfit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Why does my deep-learning validation loss increase while training loss keeps dropping?▼

Increasing validation loss alongside dropping training loss is the primary symptom of deep-learning overfit, but this skill audits epoch-level curves to rule out distribution shift, label noise, and underfitting before diagnosing true overfit.

How do I diagnose whether my Transformer or CNN is overfitting or just underfitting?▼

To diagnose overfitting versus underfitting, compare train-versus-validation loss trajectories across epochs. This audit checks distribution matches, label quality, and weight norms to separate true overfit from training plateaus or underfitting.

What is the correct order to apply regularization techniques for deep learning?▼

The correct order for deep-learning regularization prioritizes early stopping first, then data augmentation, dropout, weight decay, capacity reduction, and finally collecting more data based on the specific generalization failure diagnosed.

Can I audit overfitting for RNN and LSTM training runs using epoch-level loss curves?▼

Yes, you can audit RNN and LSTM training runs by providing epoch-level train and validation loss histories, optional weight norms, and dataset summaries to receive a trajectory-based diagnosis and prioritized remediation sequence.

How do I tell if validation performance degradation is caused by label noise instead of overfitting?▼

To tell if degradation is label noise instead of overfitting, this audit performs guardrailed triage that checks for train-validation distribution mismatches and mislabeled validation examples before recommending any regularization steps.

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