vit-module-fusion-and-training-optimization
集成UEP或Counter_Guide等模块到Vision Transformer，处理模块适配（2D转1D）、并行或条件插入逻辑，并针对新增模块调整训练超参数以防止过拟合。
customize-performance-criteria-with-examples
Rewords and paraphrases vocational training performance criteria and adds concrete workplace examples to strengthen them.
character-attribute-blending-and-enhancement
This skill is used to transfer attributes from a source character description to a target character description. It ensures the target character retains their unique integrity without degradation, removes direct references to the source character, and enhances the resulting text.
customize-performance-criteria-with-examples
Rewords or paraphrases performance criteria for training contexts and adds concrete, observable workplace examples to strengthen assessment context.
deep-learning-prediction-with-chaid-and-time-series-splitting
Executes binary classification using DNN and CNN models, with and without CHAID feature selection, using a rolling time-series training window. Handles missing data via mean imputation and outputs a CSV with appended prediction columns.
ev-recommendation-with-weighted-criteria-scoring
Recommends top electric vehicles based on user-defined technical constraints (features, range, body type, brand) and calculates a weighted percentage match score for ranking.
extract-pre-softmax-embeddings-to-dictionary
Extracts embedding vectors from the layer immediately preceding the Softmax layer of a pre-trained model (e.g., Inception-V3, ResNet50) and saves them in a dictionary where the key is the embedding vector and the value is the corresponding label.
matlab-arima-model-selection-and-forecasting
Generates MATLAB code to perform time series forecasting by testing multiple ARIMA models, selecting the best one based on AIC, and plotting the forecast.
plot-catboost-feature-importance-with-custom-styling
Generates a Python function to train a CatBoost model on a DataFrame and plot feature importances. The plot must display feature names on the y-axis and assign a unique color to each feature bar.
pytorch-accuracy-calculation-conversion-crossentropy-to-mse
Converts PyTorch training loop code from using CrossEntropyLoss to MSELoss, specifically updating the accuracy calculation logic from argmax-based comparison to rounding-based comparison to handle regression outputs.
pytorch-csv-image-data-loading-and-vgg-training
Load flattened image data from a CSV file with specific schema (label + pixel columns), create train/validation/test data loaders, and train a retrained VGG classification network.
roleplay-as-remote-viewing-bot
Roleplay as a Remote Viewing AI trained on coordinate/target pairings to describe targets, answer questions, and provide recommendations.
qe-defect-intelligence
Predicts defect-prone code using change frequency, complexity metrics, and historical bug patterns. Use when predicting defects before they escape, analyzing root causes of test failures, learning from past defect patterns, or implementing proactive quality management.
qe-learning-optimization
Optimizes QE agent performance through transfer learning, hyperparameter tuning, and pattern distillation across test domains. Use when improving agent accuracy, applying learned patterns to new projects, tuning quality thresholds, or implementing continuous improvement loops for AI-powered testing.
qe-defect-intelligence
AI-powered defect prediction, pattern learning, and root cause analysis for proactive quality management.
qe-holistic-testing-pact
Apply the Holistic Testing Model evolved with PACT (Proactive, Autonomous, Collaborative, Targeted) principles. Use when designing comprehensive test strategies for Classical, AI-assisted, Agent based, or Agentic Systems building quality into the team, or implementing whole-team quality practices.
qe-learning-optimization
Transfer learning, metrics optimization, and continuous improvement for AI-powered QE agents.
qe-test-idea-rewriting
Transform passive 'Verify X' test descriptions into active, observable test actions. Use when test ideas lack specificity, use vague language, or fail quality validation. Converts to action-verb format for clearer, more testable descriptions.
generate-inference-code-for-image-to-html-keras-model
Generates Python code to perform inference on a pre-trained Keras Image-to-HTML model, utilizing specific image preprocessing (aspect-ratio preserving resize and padding) and a greedy decoding loop to predict HTML sequences from images.