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Transfer Learning Adaptation Skill

adapting-transfer-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

Comprehensive AI/ML Development Workflow

sickn33/antigravity-awesome-skills

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

Comprehensive AI/ML Development Workflow

sickn33/antigravity-awesome-skills

A comprehensive, multi-phased workflow bundle designed for building production-grade AI and Machine Learning applications. It guides users through key stages, including AI application design, RAG implementation, autonomous agent development, robust ML pipelines, AI observability setup, and security hardening.

Apollo API Integration Quick Start

apollo-hello-world

jeremylongshore/claude-code-plugins-plus-skills

This skill provides a minimal working example for integrating with the Apollo.io API. It demonstrates the three core operations: searching for people, enriching individual contacts (to get emails/phones), and enriching entire organizations. It is ideal for developers starting a new Apollo integration, testing API setup, or learning basic data enrichment patterns.

Azure Machine Learning SDK Management

sickn33/antigravity-awesome-skills

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

Azure Machine Learning SDK Management

sickn33/antigravity-awesome-skills

This comprehensive Python SDK serves as a client library for managing the full lifecycle of Machine Learning resources on Azure Machine Learning. It allows users to programmatically handle workspaces, register data and models, provision compute clusters, define complex training jobs, and build reproducible, end-to-end MLOps pipelines.

Continuous Learning Skill Execution

cc-skill-continuous-learning

sickn33/antigravity-awesome-skills

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

Classification Model Builder

building-classification-models

jeremylongshore/claude-code-plugins-plus-skills

Automates building and evaluating classification models by analyzing datasets, selecting features, tuning hyperparameters, and reporting metrics via the classification-model-builder plugin for supervised learning requests.

Self-Learning Correction System for Claude

jeremylongshore/claude-code-plugins-plus-skills

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

AutoML Pipeline Builder

building-automl-pipelines

jeremylongshore/claude-code-plugins-plus-skills

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Code Explanation and Analysis

code-documentation-code-explain

sickn33/antigravity-awesome-skills

This skill acts as a code education expert, specializing in breaking down complex code, algorithms, or system architecture. It transforms difficult technical concepts into understandable explanations using clear narratives, visual diagrams, and step-by-step breakdowns. Ideal for creating learning materials, onboarding guides, or detailed technical reviews for developers of all levels.

Context Engineering For LLMs

context-fundamentals

sickn33/antigravity-awesome-skills

A comprehensive guide to understanding the complete state (context) available to Large Language Models during inference. Covers core components like System Prompts, Tool Definitions, Message History, and Retrieval Augmented Generation (RAG). Learning to engineer context is crucial for building robust, efficient, and reliable AI agent systems by managing the attention budget and ensuring high-signal information delivery.

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