writing-tests
Writes tests following project conventions. Handles test file organization, runtime vs type tests, table-driven tests with Test module, and type assertions with Assert API.
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Writes tests following project conventions. Handles test file organization, runtime vs type tests, table-driven tests with Test module, and type assertions with Assert API.
Synchronizes tsconfig.json paths from package.json imports field. Ensures TypeScript resolves subpath imports (#imports) correctly during development.
Refreshes README.md documentation tables including the packages table and core namespace index. Reads from package.json files and core module JSDoc.
Audits the project for consistency issues that may arise from manual editing. Checks package scripts, tsconfig paths, README tables, and other conventions.
Creates new packages in the @kitz monorepo with proper structure, configuration, and workspace integration. Handles package.json, tsconfigs, source files, and runs necessary sync scripts.
Creates commits following project conventions. Handles Conventional Commits with project-specific scopes, PR title requirements, changesets for releases, and CI validation rules.
How to run scripts in this pnpm + turbo monorepo. Covers turbo task filtering, caching, package-local scripts, and common pitfalls.
Implements functions following project conventions. Applies data-first parameter order, currying patterns with *On and *With variants, and consistent universal operation names.
Authors and manages global package scripts using the _: prefix convention. Scripts defined in root package.json are propagated to all packages with the prefix stripped.
Creates new modules within existing packages following project conventions. Handles file structure, barrel exports, namespace files, package.json imports/exports, and internal import patterns.
Python fundamentals, data structures, OOP, and data science libraries (Pandas, NumPy). Use when writing Python code, data manipulation, or algorithm implementation.
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
Docker, Kubernetes, CI/CD, model monitoring, and cloud platforms. Use for deploying ML models to production, setting up pipelines, or infrastructure.
ETL pipelines, Apache Spark, data warehousing, and big data processing. Use for building data pipelines, processing large datasets, or data infrastructure.
Neural networks, CNNs, RNNs, Transformers with TensorFlow and PyTorch. Use for image classification, NLP, sequence modeling, or complex pattern recognition.
Text processing, sentiment analysis, LLMs, and NLP frameworks. Use for text classification, named entity recognition, or language models.
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
Probability, distributions, hypothesis testing, and statistical inference. Use for A/B testing, experimental design, or statistical validation.
Supervised/unsupervised learning, model selection, evaluation, and scikit-learn. Use for building classification, regression, or clustering models.
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
Image processing, object detection, segmentation, and vision models. Use for image classification, object detection, or visual analysis tasks.
Quantization, pruning, AutoML, hyperparameter tuning, and performance optimization. Use for improving model performance, reducing size, or automated ML.
Capture PNG screenshots of dex TUI commands for debugging UI layout issues. Use when iterating on CLI interface code or debugging rendering problems.
Generate interactive HTML presentations for ANY video type (tutorials, comparisons, fact-checks, explainers, etc.). Creates self-contained, screen-recording-optimized slides with various content types including...
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