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Deep Learning Optimizer · dicklesworthstone | DecimalAI

dicklesworthstone/optimizing-deep-learning-models

SkillScore100Maintained

Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance". Trigger with phrases like 'optimize', 'performance', or 'speed up'.

AI/MLby dicklesworthstone

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/dicklesworthstone-optimizing-deep-learning-models/SKILL.md

decimalai skills pull dicklesworthstone-optimizing-deep-learning-models --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/dicklesworthstone-optimizing-deep-learning-models/SKILL.md https://app.decimal.ai/s/dicklesworthstone-optimizing-deep-learning-models/SKILL.md

Raw file: /s/dicklesworthstone-optimizing-deep-learning-models/SKILL.md ↗ · machine-readable summary at /s/dicklesworthstone-optimizing-deep-learning-models.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 13d ago

Yes — +5% lift · at higher cost+87% tokens0% turns100% of whole cases pass with skill (22/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-11	✗→✓	▲ Improved	311%	0%
case-01	✓→✓	＝ Same ✓	39%	0%
case-02	✓→✓	＝ Same ✓	40%	0%
case-03	✓→✓	＝ Same ✓	-5%	0%
case-04	✓→✓	＝ Same ✓	9%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dicklesworthstone / pi_agent_rust

tests/ext_conformance/artifacts/plugins-community/plugins/ai-ml/deep-learning-optimizer/skills/optimizing-deep-learning-models/SKILL.md

View on GitHub ↗

License MITImported 8/17/2026Version v1Bundled 8 files · scripts

Skill contents

scripts/

references/

assets/

SKILL.md

Deep Learning Optimizer

This skill provides automated assistance for deep learning optimizer tasks.

Overview

This skill provides automated assistance for deep learning optimizer tasks. This skill empowers Claude to automatically optimize deep learning models, enhancing their performance and efficiency. It intelligently applies various optimization techniques based on the model's characteristics and the user's objectives.

How It Works

Analyze Model: Examines the deep learning model's architecture, training data, and performance metrics.
Identify Optimizations: Determines the most effective optimization strategies based on the analysis, such as adjusting the learning rate, applying regularization techniques, or modifying the optimizer.
Apply Optimizations: Generates optimized code that implements the chosen strategies.
Evaluate Performance: Assesses the impact of the optimizations on model performance, providing metrics like accuracy, training time, and resource consumption.

When to Use This Skill

This skill activates when you need to:

Optimize the performance of a deep learning model.
Reduce the training time of a deep learning model.
Improve the accuracy of a deep learning model.
Optimize the learning rate for a deep learning model.
Reduce resource consumption during deep learning model training.

Examples

Example 1: Improving Model Accuracy

User request: "Optimize this deep learning model for improved image classification accuracy."

The skill will:

Analyze the model and identify potential areas for improvement, such as adjusting the learning rate or adding regularization.
Apply the selected optimization techniques and generate optimized code.
Evaluate the model's performance and report the improved accuracy.

Example 2: Reducing Training Time

User request: "Reduce the training time of this deep learning model."

The skill will:

Analyze the model and identify bottlenecks in the training process.
Apply techniques like batch size adjustment or optimizer selection to reduce training time.
Evaluate the model's performance and report the reduced training time.

Best Practices

Optimizer Selection: Experiment with different optimizers (e.g., Adam, SGD) to find the best fit for the model and dataset.
Learning Rate Scheduling: Implement learning rate scheduling to dynamically adjust the learning rate during training.
Regularization: Apply regularization techniques (e.g., L1, L2 regularization) to prevent overfitting.

Integration

This skill can be integrated with other plugins that provide model building and data preprocessing capabilities. It can also be used in conjunction with monitoring tools to track the performance of optimized models.

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

Illustrative Want to see what dicklesworthstone/optimizing-deep-learning-models would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

9/8/2026, 13:32 UTC · 22 cases

Lift

+5%

100% vs 95%

Agent turns

1.0 vs 1.0

Tokens

+87%

2,501 vs 1,339.86

fail → pass · 1pass → pass · 21

Per-case results (22)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-01 How do I fix NVIDIA CUDA driver compatibility errors when setting up a PyTorch training environment on Ubuntu 22.04?	pass→pass	19,513	20,621	+6%	1	1	0%	2,629	3,659	+39%	0	0	—
case-02 How do I set up Label Studio to annotate bounding boxes for an image dataset prior to model training?	pass→pass	16,047	17,835	+11%	1	1	0%	2,060	2,879	+40%	0	0	—
case-03 How do I build a FastAPI REST endpoint to serve real-time predictions from a trained PyTorch image classification model?	pass→pass	23,769	18,966	-20%	1	1	0%	3,746	3,564	-5%	0	0	—
case-04 When optimizing a PyTorch ResNet-50 on CIFAR-10, the validation loss plateaus around epoch 40 if using a fixed learning rate of 0.1. A naive approach is to manually drop LR by 10x at fixed intervals. What automated learning rate schedule smoothly decreases LR to zero over training?	pass→pass	16,777	15,074	-10%	1	1	0%	2,340	2,559	+9%	0	0	—
case-05 When fine-tuning BERT in PyTorch, setting standard Adam optimizer with weight_decay=0.01 yields poor regularization because standard Adam modifies gradients directly rather than decaying weights separately. Which optimizer decoupled weight decay to fix this?	pass→pass	9,378	12,720	+36%	1	1	0%	775	2,134	+175%	0	0	—
case-06 Training a ViT-Base model on an NVIDIA RTX 3090 causes Out Of Memory errors when mini-batch size exceeds 16, but training stability requires an effective batch size of 128. What technique accumulates gradients across 8 micro-batches before calling step()?	pass→pass	12,364	15,237	+23%	1	1	0%	1,426	2,444	+71%	0	0	—
case-07 To reduce deep learning training wall-clock time and memory consumption on NVIDIA Ampere GPUs, developers often consider downsampling input image resolution. What PyTorch training technique speeds up execution by utilizing Tensor Cores with FP16 or BF16 without changing image resolution?	pass→pass	13,010	14,133	+9%	1	1	0%	1,033	2,321	+125%	0	0	—
case-08 A 1000-class image classification network achieves 99% training accuracy with high confidence scores on wrong predictions. A typical response is to heavily increase L2 weight decay. What loss function modification replaces hard one-hot target vectors with soft targets to curb overconfidence?	pass→pass	9,814	13,600	+39%	1	1	0%	799	2,096	+162%	0	0	—
case-09 During training of an LSTM recurrent neural network on sequence data, gradient norms occasionally spike to over 500, causing loss values to become NaN. A naive attempt is removing layers. What optimizer technique caps gradient norms to a maximum threshold?	pass→pass	10,901	12,438	+14%	1	1	0%	1,009	2,000	+98%	0	0	—
case-10 In the early epochs of Transformer training, large Adam momentum updates with high initial learning rates cause severe training instability. What practice gradually increases the learning rate from near zero to the peak learning rate over initial steps?	pass→pass	9,457	12,927	+37%	1	1	0%	662	2,141	+223%	0	0	—
case-11 A multi-layer perceptron with 5 dense layers overfits rapidly, showing 98% train accuracy but 60% test accuracy. Instead of stopping training at epoch 2, what technique randomly zeroes neuron activations with probability p during forward passes?	fail→pass	7,945	11,412	+44%	1	1	0%	446	1,835	+311%	0	0	—
case-12 When optimizing sequence-to-sequence Transformer models where batch sizes vary or are small, batch normalization often fails due to noisy batch statistics across variable sequence lengths. What normalization technique computes statistics across feature channels per sample instead?	pass→pass	21,119	13,876	-34%	1	1	0%	666	2,185	+228%	0	0	—
case-13 To achieve better generalization near the end of training without paying the compute cost of training multiple independent model ensembles, what technique averages model weights collected across multiple final training epochs?	pass→pass	12,011	15,283	+27%	1	1	0%	981	2,310	+135%	0	0	—
case-14 When training a deep CNN for 200 epochs, validation loss stops decreasing at epoch 25 and begins rising. Rather than executing all 200 epochs, what training callback monitors validation loss and halts training when improvement ceases?	pass→pass	9,439	14,235	+51%	1	1	0%	765	2,391	+213%	0	0	—
case-15 A 50-layer deep neural network with ReLU activations experiences vanishing/exploding gradients at epoch 0 when weights are initialized from standard Gaussian distribution with stddev 1.0. What initialization method scales variance specifically for ReLU activations?	pass→pass	13,273	14,414	+9%	1	1	0%	1,262	2,332	+85%	0	0	—
case-16 Standard Stochastic Gradient Descent struggles in ravines where surface curves much more steeply in one dimension than another. Instead of setting learning rate extremely high, what velocity-based parameter update helps SGD accelerate along persistent directions?	pass→pass	10,601	13,121	+24%	1	1	0%	955	2,042	+114%	0	0	—
case-17 When fine-tuning LLMs, intermediate activation tensors consume over 80% of GPU memory during forward passes. Instead of reducing context length or pruning model weights, what technique trades extra re-computation time for memory by discarding intermediate activations?	pass→pass	10,621	14,685	+38%	1	1	0%	1,009	2,333	+131%	0	0	—
case-18 When scaling distributed PyTorch training from batch size 64 to batch size 512, keeping the original learning rate causes slow convergence. What scaling rule should be applied to the learning rate proportional to batch size increase?	pass→pass	13,735	17,113	+25%	1	1	0%	1,548	3,023	+95%	0	0	—
case-19 To stabilize training on noisy loss surfaces without extensive hyperparameter tuning, what optimizer wrapper maintains fast weights for inner loop updates and slow weights for outer loop updates?	pass→pass	12,972	16,158	+25%	1	1	0%	1,243	2,973	+139%	0	0	—
case-20 During Generative Adversarial Network (GAN) training, evaluation metrics fluctuate wildly between epochs. Rather than relying on the single last step checkpoint, what technique maintains a smoothed copy of model weights using a decay factor like 0.999?	pass→pass	11,830	15,200	+28%	1	1	0%	1,135	2,478	+118%	0	0	—
case-21 To train a PyTorch model across 4 GPUs on a single host, using legacy torch.nn.DataParallel suffers from Python GIL bottlenecks and uneven GPU utilization. What multi-process PyTorch module should be used instead?	pass→pass	11,263	13,089	+16%	1	1	0%	1,217	2,354	+93%	0	0	—
case-22 When configuring weight decay for Transformer architectures, applying decay to bias terms and LayerNorm scale/bias parameters can penalize model capacity unnecessarily. What parameter group separation strategy avoids this?	pass→pass	15,446	18,058	+17%	1	1	0%	1,771	2,969	+68%	0	0	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 95.5%
Pass rate with the skill: 100%
Cases attempted: 22
Cases counted toward lift: 22
Cases that regressed: 0
Skill version measured: v1
Run completed: 9/8/2026, 13:32 UTC

Measured example — same prompt, with vs. without the skill · from the benchmark run · case case-11

Prompt

A multi-layer perceptron with 5 dense layers overfits rapidly, showing 98% train accuracy but 60% test accuracy. Instead of stopping training at epoch 2, what technique randomly zeroes neuron activations with probability p during forward passes?

Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.

Show both answers

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run100

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Efficiency · separate axis (at equal correctness)+0% turns+83% tokens

Shown for transparency; not credited unless a verified run shows no correctness regression.

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 13 days ago

Other measured skills in the registry, with their headline benchmark lift.