Tracking Application Response Times

jeremylongshore/claude-code-plugins-plus-skills

Validates messaging consistency across website, GitHub repositories, and local documentation. Generates comprehensive read-only discrepancy reports showing where messaging conflicts or inconsistencies exist. Activates when user mentions "consistency check", "validate documentation", "check for mixed messaging", "audit content consistency", or before updating internal paperwork.

jeremylongshore/claude-code-plugins-plus-skills

Enforces complete Taskwarrior integration protocol for ALL coding tasks. Activates automatically when user mentions "taskwarrior", "task warrior", "tw", or discusses task management. Decomposes all coding work into properly tracked Taskwarrior tasks with full lifecycle: task add → task start → implementation → task done. Integrates with Timewarrior for automatic time tracking.

jeremylongshore/claude-code-plugins-plus-skills

This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.

jeremylongshore/claude-code-plugins-plus-skills

This skill enables Claude to track and manage AI/ML model versions using the model-versioning-tracker plugin. It should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control for AI/ML models. Use this skill when the user mentions "track versions", "model registry", "MLflow", or requests assistance with AI/ML model deployment and management. This skill facilitates the implementation of best practices for model versioning, automation of model workflows, and performance optimization.

jeremylongshore/claude-code-plugins-plus-skills

This skill automates the setup of machine learning experiment tracking using tools like MLflow or Weights & Biases (W&B). It is triggered when the user requests to "track experiments", "setup experiment tracking", "initialize MLflow", or "integrate W&B". The skill configures the necessary environment, initializes the tracking server (if needed), and provides code snippets for logging experiment parameters, metrics, and artifacts. It helps ensure reproducibility and simplifies the comparison of different model runs.

jeremylongshore/claude-code-plugins-plus-skills

This skill trains machine learning models using automated workflows. It analyzes datasets, selects appropriate model types (classification, regression, etc.), configures training parameters, trains the model with cross-validation, generates performance metrics, and saves the trained model artifact. Use this skill when the user requests to "train" a model, needs to evaluate a dataset for machine learning purposes, or wants to optimize model performance. The skill supports common frameworks like scikit-learn.