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Databricks MLflow Workflow

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Guides ML engineers through the Databricks secondary workflow that links Feature Store engineering, MLflow experiment tracking, model registry versioning, and serving so production-grade models can be trained and deployed.

Feature Engineering Toolkit

engineering-features-for-machine-learning

jeremylongshore/claude-code-plugins-plus-skills

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

Evernote Search Workflow

evernote-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Evernote Core Workflow B delivers a reusable search and retrieval service, covering grammar, filters, pagination, related notes, and enrichment to support search-driven features or interfaces.

Exa Core Workflow

exa-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Defines the primary Exa Core Workflow A for implementing the main use case, running core feature integrations, and handling the expected API output, manual steps, and error guidelines.

Explaining Machine Learning Models

explaining-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Feature Engineering Helper

feature-engineering-helper

jeremylongshore/claude-code-plugins-plus-skills

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

Feature Forge Workshop

Jeffallan/claude-skills

Guides structured requirements workshops to define new features, capture user value, and produce specs, EARS-format functional requirements, acceptance criteria, and implementation checklists for development planning and handoff.

Feature Importance Analyzer

feature-importance-analyzer

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance on assessing feature importance for machine learning training, covering data prep, model training, tuning, and experiment tracking while generating production-ready code, configurations, and validation instructions for best-practice compliance.

Feature Store Connector

feature-store-connector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for setting up and validating feature store connectors within ML deployment pipelines, covering best practices, code generation, and production monitoring for inference.

FireCrawl Core Workflow

firecrawl-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Executes FireCrawl primary workflow A so teams can build the main features and core integrations; trigger it with the main workflow command once auth setup is finished and API credentials are configured to drive the money-path use case.

Fireflies Core Workflow Guide

fireflies-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Executes Fireflies.ai primary Core Workflow A for main features, handling initialization, execution, and finalization while surfacing API results and errors.

Suspense Frontend Guidelines

frontend-dev-guidelines

sickn33/antigravity-awesome-skills

Defines the guardrails for senior React+TypeScript work: Suspense-first data fetching, feature-based structure, strict TypeScript, MUI styling, routing rules, and performance defaults so every new component, route, or data fetch stays maintainable and predictable.

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